In [1]:
#关闭警告信息
import warnings  
warnings.filterwarnings("ignore")
In [2]:
#测试GPU可用性
import tensorflow as tf
print('GPU可用性:',tf.test.is_gpu_available())
WARNING:tensorflow:From /tmp/ipykernel_822/2381302516.py:3: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU可用性: True
2025-05-31 18:12:45.081025: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2025-05-31 18:12:46.052509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /device:GPU:0 with 30987 MB memory:  -> device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:67:02.0, compute capability: 7.0

读取数据

In [2]:
#加载预处理后的数据
import pandas as pd
trainmap = pd.read_csv('/mnt/workspace/DNA_methylation_Data/trainmap.csv')
traindata = pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_Data.pkl')
dna_methylation_cg=pd.read_excel('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_CG.xlsx')
In [3]:
#拼接CG编号
CG_ID_str=['sample_id']+list(dna_methylation_cg.loc[:,'cpgsite'])
traindata.columns=CG_ID_str
traindata.head(10)
Out[3]:
sample_id cg00050873 cg00212031 cg00213748 cg00214611 cg00455876 cg01707559 cg02004872 cg02011394 cg02050847 ... cg12735498 cg12737588 cg12738347 cg12758090 cg12766407 cg12789522 cg12793879 cg12794168 cg12799119 cg12848808
0 train10001 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.096055 4.051632 -0.355631 1.157883 -9.210440 -2.921730 -3.472874 1.202370 -5.773449 -0.164336
1 train10002 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.451424 4.178048 -0.144221 0.944243 -3.938986 -2.732410 -3.406479 0.866223 -4.489850 -0.285874
2 train10003 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.241113 4.402578 -0.562248 0.924494 -5.773449 -2.767818 -3.619579 1.098346 -4.585271 -0.326813
3 train10004 NaN NaN NaN NaN NaN -1.398461 NaN NaN NaN ... -0.184485 3.744756 -1.087711 1.515914 -5.273603 -3.580953 -3.284902 1.179996 -4.489850 -0.570906
4 train10005 1.136022 -4.489850 NaN -3.993781 1.312567 -2.901295 -4.585271 2.804368 2.985388 ... -0.160310 3.744756 -0.866223 1.529517 -5.273603 -2.823096 -3.406479 1.294708 -4.690541 -0.132166
5 train10006 1.423834 -9.210440 1.035353 -3.837361 1.288795 -3.100385 -5.093549 2.767818 1.848299 ... 0.003999 4.178048 -1.536363 1.130596 -9.210440 -2.804368 -3.619579 1.157883 -4.807960 -0.885489
6 train10007 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.092047 4.247583 -0.200630 1.613763 -5.093549 -2.861514 -3.543689 1.109037 -5.773449 -0.164336
7 train10008 1.475799 -3.993781 NaN -4.051632 1.475799 -3.314031 -4.807960 2.785946 2.196336 ... -0.241113 4.402578 -0.532105 1.550147 -5.093549 -3.100385 -3.374769 1.125184 -4.489850 0.220849
8 train10009 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.212757 3.938986 -1.342748 1.423834 -4.585271 -3.007447 -3.472874 1.208006 -4.247583 -0.502197
9 train10010 1.515914 -4.051632 NaN -4.585271 1.174443 -2.681278 -4.112908 3.406479 2.069693 ... -0.372163 4.807960 -0.570906 1.423834 -6.811545 -2.804368 -3.472874 1.136022 -4.247583 -0.703513

10 rows × 100001 columns

数据预处理:处理空缺数据、数据拼接、转化数据类型

In [4]:
#统计数据空缺值
traindata_null=traindata.isnull().sum().sum()    #统计前10万个特征缺失值
data_sum=traindata.shape[0]*traindata.shape[1]
traindata_null_rate=traindata_null/data_sum    #计算特征缺失率
print('训练数据缺失数据量为:{0},数据总量为:{1}'.format(traindata_null,data_sum))
print('训练数据缺失率为:{0}%'.format(round(traindata_null_rate*100,4)))
训练数据缺失数据量为:46058632,数据总量为:823308233
训练数据缺失率为:5.5943%
In [5]:
#缺失数据处理
traindata.fillna(0,inplace=True)    #训练集预处理
In [6]:
# 拼接数据集
traindata=traindata.merge(trainmap[['sample_id', 'age', 'gender', 'sample_type', 'disease']],on='sample_id',how='left')
In [8]:
pd.isnull(traindata).sum()
Out[8]:
sample_id       0
cg00050873      0
cg00212031      0
cg00213748      0
cg00214611      0
               ..
cg12848808      0
age             0
gender         69
sample_type     0
disease         0
Length: 100005, dtype: int64
In [10]:
traindata['gender'].value_counts()
Out[10]:
gender
F    4409
M    3755
Name: count, dtype: int64
In [11]:
traindata.shape
Out[11]:
(8233, 100005)
In [9]:
#统计数据中患病数据和患病数据汇总
sample_type_sum=traindata['sample_type'].value_counts()    #统计患病数据总量
print('-----------------患病数据总量----------------')
print(sample_type_sum)
disease_sum=traindata['disease'].value_counts()    #汇总患病数据
print('-----------------患病数据汇总----------------')
print(disease_sum)
-----------------患病数据总量----------------
sample_type
control           6266
disease tissue    1967
Name: count, dtype: int64
-----------------患病数据汇总----------------
disease
control                 6266
Alzheimer's disease      737
schizophrenia            381
Parkinson's disease      266
rheumatoid arthritis     159
stroke                   147
Huntington's disease     135
Graves' disease           58
type 2 diabetes           46
Sjogren's syndrome        38
Name: count, dtype: int64
In [9]:
#读取卒中DNA甲基化数据
Stroke_Data=traindata.loc[traindata.loc[:,'disease']=='stroke',:]
Stroke_Data.shape
Out[9]:
(147, 100005)
In [10]:
#读取卒中DNA甲基化数据
Normal_Data=traindata.loc[traindata.loc[:,'disease']=='control',:]
Normal_Data.shape
Out[10]:
(6266, 100005)
In [11]:
#整合数据
Analysis_Data=pd.concat([Stroke_Data,Normal_Data],axis=0)
Analysis_Data.shape
Out[11]:
(6413, 100005)
In [12]:
#数据类型转化
disease_mapping = {
    'control': 0,
    'stroke': 1,
}    #构建数据对应关系
sample_type_mapping = {'control': 0, 'disease tissue': 1}
gender_mapping = {'F': 0, 'M': 1}
#训练集转化
Analysis_Data['disease_encode']=Analysis_Data['disease'].map(disease_mapping)
Analysis_Data['sample_type_encode']=Analysis_Data['sample_type'].map(sample_type_mapping)
Analysis_Data['gender_encode']=Analysis_Data['gender'].map(gender_mapping)
In [18]:
#输出仅包含卒中和正常组的数据
Analysis_Data.to_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Analysis_Data.to_csv('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.csv')

读取数据

In [1]:
import pandas as pd
Data=pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Data.head(10)
Out[1]:
sample_id cg00050873 cg00212031 cg00213748 cg00214611 cg00455876 cg01707559 cg02004872 cg02011394 cg02050847 ... cg12794168 cg12799119 cg12848808 age gender sample_type disease disease_encode sample_type_encode gender_encode
8040 train18041 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.804620 -3.406479 -1.991711 78.0 F disease tissue stroke 1 1 0.0
8041 train18042 2.049755 -3.472874 0.000000 -3.837361 0.974195 -2.375206 -3.659672 3.790034 4.690541 ... 0.871025 -3.993781 -1.642924 52.0 M disease tissue stroke 1 1 1.0
8042 train18043 2.196336 -2.985388 0.000000 -2.963760 0.984269 -2.324893 -3.790034 3.790034 4.247583 ... 0.994389 -4.051632 -2.059685 92.0 M disease tissue stroke 1 1 1.0
8043 train18044 0.000000 0.000000 0.000000 0.000000 0.000000 -1.242189 0.000000 0.000000 0.000000 ... 1.392179 -3.886935 -2.648415 87.0 F disease tissue stroke 1 1 0.0
8044 train18045 2.632328 -3.284902 2.120936 -3.938986 1.628272 -2.616463 -3.886935 2.942546 4.247583 ... 1.621000 -3.701353 -2.681278 87.0 M disease tissue stroke 1 1 1.0
8045 train18046 2.264792 -2.942546 0.000000 -3.052934 0.979226 -1.972920 -3.659672 3.543689 4.489850 ... 1.191150 -3.619579 -1.991711 41.0 M disease tissue stroke 1 1 1.0
8046 train18047 0.000000 0.000000 0.000000 0.000000 0.000000 -1.098346 0.000000 0.000000 0.000000 ... 1.436670 -3.744756 -2.120936 78.0 F disease tissue stroke 1 1 0.0
8047 train18048 1.963624 -4.178048 0.000000 -4.178048 0.124134 -2.324893 -5.492861 3.076403 4.940737 ... 1.635580 -3.837361 -1.954393 80.0 M disease tissue stroke 1 1 1.0
8048 train18049 1.874054 0.000000 0.000000 0.000000 -0.298130 -1.798190 -4.112908 3.993781 4.585271 ... 1.703036 -3.406479 -2.131436 69.0 M disease tissue stroke 1 1 1.0
8049 train18050 1.462650 -3.256540 0.000000 -4.051632 0.000000 -2.163468 -5.273603 2.921730 4.402578 ... 1.489058 -2.785946 -1.963624 75.0 M disease tissue stroke 1 1 1.0

10 rows × 100008 columns

数据预处理和数据类型转化已经完成,随后进行特征选择。首先使用PCA算法计算保留95%和99%信息的维度,并将数据降维至3维可视化数据分布。对甲基化位点采用方差过滤,高相关过滤和lasso回归.

In [2]:
#采用PCA算法计算保留95%和99%信息的weidu
Methylation=Data.iloc[:,1:-7]
Methylation.shape
Out[2]:
(6413, 100000)
In [ ]:
#执行PCA算法-计算保留99%信息时的维度
from sklearn.decomposition import PCA
Methylaion_PCA=PCA(n_components=0.99)
PCA_Methylation_99=Methylaion_PCA.fit_transform(Methylation)    #数据降维
PCA_Methylation_99.shape
In [4]:
#计算可解释方差比
import numpy as np
import matplotlib.pyplot as plt
Explain_variance=np.cumsum(Methylaion_PCA.explained_variance_ratio_)
#绘图
Explain_variance_plt=plt.figure(dpi=300)
Epl_variance=Explain_variance_plt.add_subplot(111)
Epl_variance.set_title('Explainable variance ratio curve')
Epl_variance.grid(color='black',linestyle='-.',alpha=0.2)
Epl_variance.plot(np.arange(1,len(Explain_variance)+1,1,dtype=int),Explain_variance,color='orange',linestyle='-')
#添加95%可解释方差曲线
Epl_variance.scatter(x=2500,y=0.95,color='green',marker='o')
Epl_variance.axhline(y=0.95, color='green', linestyle='-.')
Epl_variance.axvline(x=2500, color='green', linestyle='-.', label='Retain 95 per cent of explainable variance')
#添加99%可解释方差曲线
Epl_variance.scatter(3973,0.99,color='red',marker='o')
Epl_variance.axhline(y=0.99, color='red', linestyle='-.')
Epl_variance.axvline(x=3973, color='red', linestyle='-.', label='Retain 99 per cent of explainable variance')
Epl_variance.set_xlabel('n_components')
Epl_variance.set_ylabel('explained_variance_ratio')
plt.legend()
plt.show()
In [5]:
#使用PCA将数据降维至3维
from sklearn.decomposition import PCA
from pandas import DataFrame
PCADisplay3D=PCA(n_components=3)
Methylation3D_PCA=PCADisplay3D.fit_transform(Methylation)    #PCA3D降维
Methylation3D_PCA=DataFrame(Methylation)
Methylation3D_PCA['Disease_Encoder']=Data.loc[:,'disease_encode']
In [6]:
#绘制3D散点图
import matplotlib.pyplot as plt  
from mpl_toolkits.mplot3d import Axes3D
NormalData=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==0,:]
Stroke_Data=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==1,:]
OA_X,OA_Y,OA_Z=NormalData.iloc[:,0],NormalData.iloc[:,1],NormalData.iloc[:,2]
CON_X,CON_Y,CON_Z=Stroke_Data.iloc[:,0],Stroke_Data.iloc[:,1],Stroke_Data.iloc[:,2]
# 创建3D图形对象
fig=plt.figure(dpi=300)  
ax=fig.add_subplot(111, projection='3d')
ax.scatter(CON_X,CON_Y,CON_Z,color='green',label='Stroke')
ax.scatter(OA_X,OA_Y,OA_Z,color='orange',label='Normal')
# 设置标签  
ax.set_xlabel('Faeture 0')  
ax.set_ylabel('Feature 1')  
ax.set_zlabel('Feature 2')
plt.legend()
# 绘制投影到XY平面  
plt.figure() 
plt.title('XY plane projection')
plt.scatter(OA_X,OA_Y,color='orange',label='Normal')  
plt.scatter(CON_X,CON_Y,color='green',label='Stroke')  
plt.xlabel('Feature 0')  
plt.ylabel('Feature 1') 
plt.legend()
# 绘制投影到XZ平面  
plt.figure() 
plt.title('XZ plane projection')
plt.scatter(OA_X,OA_Z,color='orange',label='Normal')  
plt.scatter(CON_X,CON_Z,color='green',label='Stroke')  
plt.xlabel('Feature 0')  
plt.ylabel('Feature 2')
plt.legend()
# 绘制投影到YZ平面  
plt.figure() 
plt.title('YZ plane projection')
plt.scatter(OA_Y,OA_Z,color='orange',label='Normal')  
plt.scatter(CON_Y,CON_Z,color='green',label='Stroke')  
plt.xlabel('Feature 1')  
plt.ylabel('Feature 2')
plt.legend()
plt.show()
In [3]:
#对数据采取低方差过滤算法
from pandas import DataFrame
from sklearn.feature_selection import VarianceThreshold
Variance_Selector=VarianceThreshold(threshold=0.5)
VarSele_Data=Variance_Selector.fit_transform(Methylation)    #执行低方差过滤
VarSele_Data=DataFrame(VarSele_Data)
VarSele_Data.columns=Variance_Selector.get_feature_names_out()
VarSele_Data.head(10)
Out[3]:
cg00050873 cg00212031 cg00214611 cg01707559 cg02004872 cg02011394 cg02050847 cg02233190 cg02494853 cg02839557 ... cg12700439 cg12711755 cg12712409 cg12719753 cg12725420 cg12727528 cg12732791 cg12737588 cg12766407 cg12848808
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -6.163916 0.000000 ... 2.715111 2.921730 1.417454 2.804368 -4.112908 -5.773449 1.152395 5.773449 -5.492861 -1.991711
1 2.049755 -3.472874 -3.837361 -2.375206 -3.659672 3.790034 4.690541 -3.701353 -3.406479 -3.886935 ... 2.375206 3.619579 2.241551 2.600812 -2.349789 -4.051632 0.909791 4.489850 -4.690541 -1.642924
2 2.196336 -2.985388 -2.963760 -2.324893 -3.790034 3.790034 4.247583 -3.701353 -3.790034 -2.901295 ... 2.985388 2.324893 2.049755 2.100192 -1.848299 -4.178048 1.045719 4.690541 -5.273603 -2.059685
3 0.000000 0.000000 0.000000 -1.242189 0.000000 0.000000 0.000000 0.000000 -4.322159 0.000000 ... 2.570129 2.388119 2.010774 2.300497 -2.264792 -3.993781 1.423834 4.402578 -5.093549 -2.648415
4 2.632328 -3.284902 -3.938986 -2.616463 -3.886935 2.942546 4.247583 -4.585271 -3.837361 -2.525567 ... 2.388119 3.659672 1.695385 2.585369 -2.059685 -4.322159 1.848299 5.492861 -4.585271 -2.681278
5 2.264792 -2.942546 -3.052934 -1.972920 -3.659672 3.543689 4.489850 -2.540233 -2.823096 -2.985388 ... 3.029956 3.507691 2.349789 2.230092 -2.069693 -4.690541 1.152395 4.807960 -5.773449 -1.991711
6 0.000000 0.000000 0.000000 -1.098346 0.000000 0.000000 0.000000 0.000000 -4.178048 0.000000 ... 2.732410 3.284902 1.814692 2.264792 -2.230092 -4.402578 1.823018 4.690541 -5.273603 -2.120936
7 1.963624 -4.178048 -4.178048 -2.324893 -5.492861 3.076403 4.940737 -2.401172 -3.619579 -3.149987 ... 2.555085 3.619579 1.790011 2.555085 -1.848299 -4.402578 1.300641 5.273603 -5.093549 -1.954393
8 1.874054 0.000000 0.000000 -1.798190 -4.112908 3.993781 4.585271 -3.659672 -3.314031 -3.149987 ... 2.767818 3.472874 1.522701 2.570129 -1.781880 -4.489850 0.949208 4.940737 -5.273603 -2.131436
9 1.462650 -3.256540 -4.051632 -2.163468 -5.273603 2.921730 4.402578 -2.511081 -2.921730 -2.942546 ... 2.767818 2.555085 1.945225 3.149987 -1.404768 -6.163916 0.000000 4.489850 -5.773449 -1.963624

10 rows × 63575 columns

In [6]:
#构建特征方差数据表
VarianseData=DataFrame()
VarianseData['MetaBolite']=Variance_Selector.feature_names_in_
VarianseData['Variances']=Variance_Selector.variances_
VarianseData.shape
Out[6]:
(100000, 2)
In [9]:
#输出甲基化位点方差值
VarianseData.to_excel('/mnt/workspace/Run_Result/Vraince_result.xlsx')
In [4]:
#执行基于F分布的方差分析进行高相关过滤
from sklearn.feature_selection import SelectKBest, f_classif
Selectk=SelectKBest(score_func=f_classif,k=3973)
FselectorData=Selectk.fit_transform(VarSele_Data,Data.loc[:,'disease_encode'])
FselectorData=pd.DataFrame(FselectorData)
feature_nameindex=Selectk.get_support(indices=True)    #获取特征索引
feature_names=VarSele_Data.columns    #获取特征名
Kfeature_names=[feature_names[i] for i in feature_nameindex]
FselectorData.columns=Kfeature_names
FselectorData.head(10)
Out[4]:
cg01404988 cg02393514 cg03315431 cg04065558 cg04294190 cg04490290 cg04630982 cg04667267 cg04931939 cg05032098 ... cg11183227 cg11353598 cg11540553 cg12031275 cg12160578 cg12309653 cg12368188 cg12379383 cg12414181 cg12627870
0 -1.536363 -1.557086 1.146922 -4.807960 1.695385 -1.141464 -0.132166 -0.934347 -2.785946 -0.540693 ... 0.739857 -1.179996 4.112908 -0.566575 -2.142023 -4.112908 -3.938986 -2.388119 -2.664729 -3.100385
1 -2.496772 -2.241551 2.152700 -4.489850 0.000000 0.000000 0.000000 0.000000 0.000000 -1.790011 ... 1.354970 -2.218738 3.701353 -1.522701 -1.954393 -3.076403 -3.886935 -1.749816 -2.362431 -2.749976
2 -2.482634 -1.628272 2.288480 -3.993781 0.204671 -2.468664 -1.680203 -2.152700 -2.901295 -2.001208 ... 1.536363 -2.163468 3.790034 0.188519 -2.120936 -2.901295 -3.374769 -1.710727 -3.701353 -2.616463
3 -3.149987 -1.954393 1.114405 -4.112908 2.089945 -0.188519 -0.949208 -1.174443 -3.256540 -0.851873 ... 2.100192 -2.454856 3.406479 -1.430239 -2.881228 -3.543689 -3.886935 -2.362431 -3.938986 -2.454856
4 -2.921730 -2.511081 2.525567 -3.993781 1.592260 -2.337277 -1.856830 -1.672671 -3.228904 -2.767818 ... 1.404768 -2.010774 3.886935 0.823416 -2.253116 -3.007447 -3.439160 -1.814692 -3.343970 -2.414368
5 -2.600812 -1.650305 2.616463 -4.051632 1.765757 -3.201956 -1.657722 -1.650305 -2.823096 -1.991711 ... 2.441207 -1.219330 4.489850 -0.216802 -2.648415 -2.732410 -3.314031 -1.564056 -1.991711 -2.732410
6 -2.861514 -0.823416 0.989323 -3.507691 2.030118 -0.347383 -0.100063 0.472506 -2.881228 -1.185564 ... 1.798190 -2.349789 3.837361 3.580953 -2.842141 -4.112908 -3.837361 -2.496772 -2.312634 -2.648415
7 -3.052934 -2.454856 2.230092 -3.439160 -1.294708 0.000000 -2.100192 0.000000 -1.578093 -2.362431 ... 1.430239 -2.585369 3.374769 4.178048 -1.814692 -2.414368 -3.886935 -2.767818 -2.312634 -2.482634
8 -2.496772 -2.427712 2.276579 -3.175661 -0.964166 0.000000 -1.695385 0.000000 -2.375206 -2.648415 ... 1.449607 -2.375206 3.659672 -1.806416 -2.079779 -2.414368 -3.580953 -2.441207 -2.842141 -2.715111
9 -2.525567 -2.600812 2.616463 -3.284902 -1.271175 0.000000 0.000000 0.000000 0.000000 -1.918093 ... 1.152395 -1.635580 3.507691 0.000000 -1.606561 -2.100192 -3.029956 -2.100192 -2.069693 -2.616463

10 rows × 3973 columns

In [8]:
#获取相关信息
KBestF_info=DataFrame()
KBestF_info['Feature']=Kfeature_names    #载入特征
KBestF_info['scores']=Selectk.scores_[:3973]
KBestF_info['P value']=Selectk.pvalues_[:3973]
KBestF_info.shape
Out[8]:
(3973, 3)
In [9]:
#输出相关计算结果
KBestF_info.to_excel('/mnt/workspace/Run_Result/FCorr_With_Methylation.xlsx')
In [5]:
#采用Lasso算法进行特征选择
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
Target=Data.loc[:,'disease_encode']
# 数据标准化  
scaler=StandardScaler()  
X_scaled=scaler.fit_transform(FselectorData)
# 拆分数据集  
X_train,X_test,y_train,y_test=train_test_split(X_scaled,Target, test_size=0.3, random_state=2024)
lasso_model = LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)    # 设置Lasso模型 
lasso_model.fit(X_train,y_train)    # 训练模型  
Out[5]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In [6]:
#评估Lasso模型性能
print('Lasso模型训练集Accuracy为:',accuracy_score(y_train,lasso_model.predict(X_train)))
print('Lasso模型测试集Accuracy为:',accuracy_score(y_test,lasso_model.predict(X_test)))
Lasso模型训练集Accuracy为: 1.0
Lasso模型测试集Accuracy为: 0.9994802494802495
In [10]:
#查看模型混淆矩阵
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [11]:
#查看训练集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_train,target=y_train)
In [12]:
#查看测试集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_test,target=y_test)
In [13]:
import joblib
joblib.dump(lasso_model,'/mnt/workspace/Models/Lasso.pkl')
Out[13]:
['/mnt/workspace/Models/Lasso.pkl']
In [15]:
#提取特征并进行特征选择
import numpy as np
LassoFeature_Index=np.where(lasso_model.coef_!=0)[1]
LassoFeature=FselectorData.iloc[:,LassoFeature_Index]
LassoFeature.shape
Out[15]:
(6413, 70)
In [16]:
import matplotlib.pyplot as plt
Coef=plt.figure(dpi=300)
Coef_ax=Coef.add_subplot(111)
Coef_ax.grid(color='black',linestyle='-.',alpha=0.2)
Coef_ax.plot(LassoFeature_Index,pd.DataFrame(lasso_model.coef_[0][LassoFeature_Index]))
Coef_ax.set_xlabel('CG_index')
Coef_ax.set_ylabel('Coef')
Coef_ax.set_xticklabels(LassoFeature_Index)
plt.show()
/tmp/ipykernel_10044/197567409.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  Coef_ax.set_xticklabels(LassoFeature_Index)

使用Lasso验证DNA甲基化位点特征选择后的结果

In [17]:
#使用特征选择后的模型验证模型精度-输入[batchs,70],输出[batch,1]
X_TrainEv,X_testEv,y_trainEv,y_testEv=train_test_split(LassoFeature,Target, test_size=0.3, random_state=2024)
lasso_modelEvaluateD=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)
lasso_modelEvaluateD.fit(X_TrainEv,y_trainEv)
Out[17]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In [18]:
#训练集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_TrainEv,y_trainEv)
In [19]:
#测试集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_testEv,y_testEv)
In [20]:
#使用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold,cross_val_score
from sklearn.model_selection import cross_validate
def Model_Evelate_CV(Model,Data,Target):
    cv=KFold(n_splits=10, shuffle=True, random_state=2024)
    kv_scores =cross_validate(Model,Data,Target,cv=cv,scoring='accuracy',
                          return_train_score=True)
    print('Fit_time:',kv_scores['fit_time'])
    print('Mean Fit_time:',kv_scores['fit_time'].mean())
    print('score_time:',kv_scores['score_time'])
    print('Mean score_time:',kv_scores['score_time'].mean())
    print('train_score:',kv_scores['train_score'])
    print('Mean train_score:',kv_scores['train_score'].mean())
    print('test_score:',kv_scores['test_score'])
    print('Mean test_score:',kv_scores['test_score'].mean())
#用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
lasso_modelEvaluate=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)    # 设置Lasso模型
Model_Evelate_CV(Model=lasso_modelEvaluate,Data=LassoFeature,Target=Target)
Fit_time: [0.15090775 0.20123768 0.1979394  0.19683361 0.19801974 0.19604516
 0.19239807 0.17037344 0.16579437 0.18889141]
Mean Fit_time: 0.1858440637588501
score_time: [0.00298166 0.01086545 0.00288725 0.00284386 0.00290656 0.00284505
 0.00278687 0.00281239 0.00273204 0.00283217]
Mean score_time: 0.003649330139160156
train_score: [0.99930688 0.9991336  0.99930688 0.9989605  0.99948025 0.999307
 0.99948025 0.99913375 0.99948025 0.99913375]
Mean train_score: 0.9992723102452785
test_score: [0.99844237 0.99844237 1.         0.99843994 0.99687988 0.99843994
 1.         0.99687988 0.99843994 0.99687988]
Mean test_score: 0.9982844173580029
In [21]:
#整合并输出特征选择结果
CG_Feature=Data.iloc[:,LassoFeature_Index]
DNA_Methylstion_Feature=pd.concat([CG_Feature,Data.iloc[:,-7:]],axis=1)
DNA_Methylstion_Feature.shape
Out[21]:
(6413, 77)
In [22]:
DNA_Methylstion_Feature.to_excel('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.xlsx','UTF-8')
In [23]:
DNA_Methylstion_Feature.to_csv('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.csv')
In [24]:
joblib.dump(lasso_modelEvaluateD,'/mnt/workspace/Models/lasso_modelEvaluate.pkl')
Out[24]:
['/mnt/workspace/Models/lasso_modelEvaluate.pkl']

经过低方差过滤,基于F分布的相关分析和Lasso回归,获得了70个甲基化位点作为特征子集。

In [3]:
#读取DNA甲基化位点数据
import pandas as pd
MethylationData=pd.read_excel('Data/DNA_Methylstion_Feature.xlsx')
MethylationData.head(10)
Out[3]:
Unnamed: 0 cg03706273 cg00412368 cg01370179 cg01522249 cg01999212 cg03718079 cg03977822 cg05100261 cg05876899 ... cg01446576 cg01550055 cg01717973 age gender sample_type disease disease_encode sample_type_encode gender_encode
0 8040 -4.112908 -0.015997 -1.557086 0.372163 -2.525567 -0.851873 -0.124134 -0.832866 2.921730 ... -2.570129 -1.585160 -3.052934 78.0 F disease tissue stroke 1 1 0.0
1 8041 -2.767818 -1.417454 -1.417454 0.000000 -4.585271 -2.648415 -0.489446 -3.938986 3.228904 ... -3.744756 -1.247949 -3.886935 52.0 M disease tissue stroke 1 1 1.0
2 8042 -3.374769 -2.020410 -1.529517 1.650305 -4.112908 -2.362431 -0.430440 -4.940737 3.076403 ... -4.051632 -1.536363 -5.273603 92.0 M disease tissue stroke 1 1 1.0
3 8043 -4.807960 -0.339147 -1.814692 -0.744433 -2.985388 -1.798190 0.298130 -0.372163 3.374769 ... -4.585271 -1.757764 -4.178048 87.0 F disease tissue stroke 1 1 0.0
4 8044 -4.402578 -2.555085 -1.665178 1.734052 -3.507691 -3.149987 -0.128149 -4.247583 3.228904 ... -4.247583 -1.726235 -3.993781 87.0 M disease tissue stroke 1 1 1.0
5 8045 -3.149987 -1.831393 -1.599394 1.443126 -2.732410 -3.175661 -0.076021 -4.112908 3.314031 ... -4.112908 -1.253727 -4.112908 41.0 M disease tissue stroke 1 1 1.0
6 8046 -3.619579 -0.112095 -1.119787 -0.076021 -2.823096 -1.982282 0.164336 -0.019997 3.124904 ... -4.585271 -1.271175 -3.837361 78.0 F disease tissue stroke 1 1 0.0
7 8047 -4.051632 -2.079779 -1.550147 0.000000 -4.402578 -3.837361 -0.343263 -4.807960 3.284902 ... -4.322159 -1.324576 -4.051632 80.0 M disease tissue stroke 1 1 1.0
8 8048 -3.076403 -1.781880 -1.456115 2.241551 -3.837361 -2.441207 -0.285874 -5.492861 3.201956 ... -4.247583 -1.157883 -4.322159 69.0 M disease tissue stroke 1 1 1.0
9 8049 -2.664729 -1.718460 -1.392179 2.131436 -3.701353 -2.414368 -0.056003 -5.492861 3.543689 ... -3.659672 -0.974195 -3.886935 75.0 M disease tissue stroke 1 1 1.0

10 rows × 78 columns

统计和处理空缺数据

In [4]:
#统计甲基化数据
pd.isnull(MethylationData.iloc[:,:-7]).sum().sum()
Out[4]:
0
In [5]:
#统计其他附加数据
pd.isnull(MethylationData.iloc[:,-7:]).sum()
Out[5]:
age                    0
gender                68
sample_type            0
disease                0
disease_encode         0
sample_type_encode     0
gender_encode         68
dtype: int64
In [6]:
from collections import Counter
print(Counter(MethylationData['gender']))
Counter({'F': 3387, 'M': 2958, nan: 68})
In [7]:
MethylationData['gender'].fillna('M',inplace=True)
MethylationData['gender_encode'].fillna('1',inplace=True)
In [8]:
MethylationData['gender'].hist()
Out[8]:
<AxesSubplot:>
In [9]:
print(Counter(MethylationData['gender']))
Counter({'F': 3387, 'M': 3026})
In [10]:
pd.isnull(MethylationData).sum().sum()
Out[10]:
0

提取算法可处理的数据进行机器学习分析,筛选特征甲基化位点

In [11]:
Methylation=MethylationData.iloc[:,1:-7]    #甲基化数据
MapData=MethylationData.loc[:,['age','gender_encode','disease_encode']]    #其他附加数据
MLData=pd.concat([Methylation,MapData],axis=1)    #数据合并
MLData.shape
Out[11]:
(6413, 73)

由于正常样本与卒中样本总量差异较大,因此使用数据重采样算法解决数据类别分布不均衡问题。 数据重采样策略针对训练集进行,外部测试集不使用数据重采样。同时,数据重采样后的训练集采用10-折交叉验证训练,独立外部测试集测试。

In [12]:
#统计和汇总疾病数据分布
print(Counter(MethylationData['disease']))
MethylationData['disease'].hist()
Counter({'control': 6266, 'stroke': 147})
Out[12]:
<AxesSubplot:>

编写模型训练函数——常规训练

In [13]:
#查看模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [14]:
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def PR_Curve(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024)    #划分数据集
    predict_score=Model.predict_proba(X_test)[:, 1]    #获取概率值
    predict=Model.predict(X_test)     #获取预测标签
    accuracy=accuracy_score(y_test,predict)
    precision, recall, thresholds = precision_recall_curve(y_test, predict_score)    #计算PR曲线
    PR=DataFrame()    #将PR曲线数据合并到DataFrame
    #PR['thresholds']=thresholds
    PR['recall']=recall
    PR['precision']=precision
    return PR,accuracy
In [15]:
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def ROC_Curve(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024)    #划分数据集
    predict_score=Model.predict_proba(X_test)[:, 1]    #获取概率值
    fpr, tpr, thresholds = roc_curve(y_test, predict_score)
    roc_auc = auc(fpr, tpr)    #计算AUC
    ROC=DataFrame()    #将PR曲线数据合并到DataFrame
    #ROC['thresholds']=thresholds
    ROC['tpr']=tpr
    ROC['fpr']=fpr
    return ROC,roc_auc
In [16]:
#编写模型训练评估函数
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTestModel(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024)    #划分数据集
    Model.fit(X_train,y_train)    #训练模型
    TrainPredict=Model.predict(X_train)    #计算训练集指标
    TrainPrecision=precision_score(y_train,TrainPredict)
    TrainRecall=recall_score(y_train,TrainPredict)
    TrainF1=f1_score(y_train,TrainPredict)
    TrainAcuracy=accuracy_score(y_train,TrainPredict)
    print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(TrainPrecision,TrainRecall,TrainF1,TrainAcuracy))
    TestPredict=Model.predict(X_test)    #测试集预测结果
    TestPrecision=precision_score(y_test,TestPredict)
    TestRecall=recall_score(y_test,TestPredict)
    TestF1=f1_score(y_test,TestPredict)
    TestAccuracy=accuracy_score(y_test,TestPredict)
    print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
    print('-------------------测试集混淆举证-------------------')
    Display_ConfusionMatrix(model=Model,data=X_test,target=y_test)

编写模型训练函数——数据重采样

In [17]:
#绘制模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Computer_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [18]:
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def Computer_PRCurve(Model,Data,Label):
    predict_score=Model.predict_proba(Data)[:, 1]    #获取概率值
    predict=Model.predict(Data)     #获取预测标签
    accuracy=accuracy_score(Label,predict)
    precision, recall, thresholds = precision_recall_curve(Label, predict_score)    #计算PR曲线
    PR=DataFrame()    #将PR曲线数据合并到DataFrame
    PR['recall']=recall
    PR['precision']=precision
    return PR,accuracy
In [19]:
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def Computer_ROCCurve(Model,Data,Label):
    predict_score=Model.predict_proba(Data)[:, 1]    #获取概率值
    fpr, tpr, thresholds = roc_curve(Label, predict_score)
    roc_auc = auc(fpr, tpr)    #计算AUC
    ROC=DataFrame()    #将PR曲线数据合并到DataFrame
    ROC['tpr']=tpr
    ROC['fpr']=fpr
    return ROC,roc_auc
In [20]:
#模型训练-评估函数
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split,KFold
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTrainModelCV(Resmaple,Model,Data,Label,cv):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024)    #划分数据集
    X_train_Re,y_train_Re=Resmaple.fit_resample(X_train,y_train)
    print('resample shape:',Counter(y_train_Re))
    kf=KFold(n_splits=cv, shuffle=True, random_state=2025)
    Precision_fold,Recall_fold,F1Score_fold,Accuracy_fold=[],[],[],[]
    ValidPrecision_fold,ValidRecall_fold,ValidF1Score_fold,ValidAccuracy_fold=[],[],[],[]
    for train_index,valid_index in kf.split(X_train_Re):
        # 划分出当前折的训练和验证数据
        X_fold_train, X_fold_val = X_train_Re.iloc[train_index,:], X_train_Re.iloc[valid_index,:]
        y_fold_train, y_fold_val = y_train_Re.iloc[train_index], y_train_Re.iloc[valid_index]
        Model.fit(X_fold_train,y_fold_train)    #训练模型
        # Train Score
        TrainPredict_fold=Model.predict(X_fold_train)    #计算训练集指标
        TrainPrecision=precision_score(y_fold_train,TrainPredict_fold)
        Precision_fold.append(TrainPrecision)
        TrainRecall=recall_score(y_fold_train,TrainPredict_fold)
        Recall_fold.append(TrainRecall)
        TrainF1=f1_score(y_fold_train,TrainPredict_fold)
        F1Score_fold.append(TrainF1)
        TrainAcuracy=accuracy_score(y_fold_train,TrainPredict_fold)
        Accuracy_fold.append(TrainAcuracy)
        # Valid Score
        ValidPredict_fold=Model.predict(X_fold_val)    #计算训练集指标
        ValidPrecision=precision_score(y_fold_val,ValidPredict_fold)
        ValidPrecision_fold.append(ValidPrecision)
        ValidRecall=recall_score(y_fold_val,ValidPredict_fold)
        ValidRecall_fold.append(ValidRecall)
        ValidF1=f1_score(y_fold_val,ValidPredict_fold)
        ValidF1Score_fold.append(ValidF1)
        ValidAcuracy=accuracy_score(y_fold_val,ValidPredict_fold)
        ValidAccuracy_fold.append(ValidAcuracy)
    print('Model train Precision of flod:',Precision_fold)
    print('Model train Recall of flod:',Recall_fold)
    print('Model train F1 Score of flod:',F1Score_fold)
    print('Model train Accuracy of flod:',Accuracy_fold)
    print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(Precision_fold),np.mean(Recall_fold),
                                                                           np.mean(F1Score_fold),np.mean(Accuracy_fold)))
    print('Model valid Precision of flod:',ValidPrecision_fold)
    print('Model valid train Recall of flod:',ValidRecall_fold)
    print('Model valid train F1 Score of flod:',ValidF1Score_fold)
    print('Model valid train Accuracy of flod:',ValidAccuracy_fold)
    print('模型Valid集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(ValidPrecision_fold),np.mean(ValidRecall_fold),
                                                                           np.mean(ValidF1Score_fold),np.mean(ValidAccuracy_fold)))
    Model.fit(X_train_Re,y_train_Re)
    TestPredict=Model.predict(X_test)    #测试集预测结果
    TestPrecision=precision_score(y_test,TestPredict)
    TestRecall=recall_score(y_test,TestPredict)
    TestF1=f1_score(y_test,TestPredict)
    TestAccuracy=accuracy_score(y_test,TestPredict)
    print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
    print('-------------------测试集混淆举证-------------------')
    Computer_ConfusionMatrix(model=Model,data=X_test,target=y_test)
    PR,accuracy=Computer_PRCurve(Model=Model,Data=X_test,Label=y_test)
    ROC,roc_auc=Computer_ROCCurve(Model=Model,Data=X_test,Label=y_test)
    return PR,accuracy,ROC,roc_auc,Model

划分数据集并测试欠采样算法.

In [21]:
#切割数据
MLTestData=MLData.iloc[:,:-1]
MLTestLabel=MLData.iloc[:,-1]
In [22]:
#数据归一化
from pandas import DataFrame
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ColumnsNames=MLTestData.columns
Stand=StandardScaler()
StandData=Stand.fit_transform(MLTestData)
MinMax=MinMaxScaler(feature_range=(0,1))
MinMaxData=MinMax.fit_transform(StandData)
MLTestData=DataFrame(MinMaxData)
MLTestData.columns=ColumnsNames
In [23]:
#使用随机森林算法作为衡量基准测试数据重采样算法性能
from sklearn.ensemble import RandomForestClassifier
Forest=RandomForestClassifier(random_state=2024)
RunTestModel(Model=Forest,Data=MLTestData,Label=MLTestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792
-------------------测试集混淆举证-------------------
In [24]:
#计算PR和ROC曲线
Forest_PR,Forest_Accuracy=PR_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_ROC,Forest_ROC_AUC=ROC_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_PR.to_excel('Data/PR Curve/Resample/Forest_PR.xlsx')
Forest_ROC.to_excel('Data/ROC Curve/Resample/Forest_ROC.xlsx')
In [25]:
#使用CNN算法进行欠采样
from imblearn.under_sampling import CondensedNearestNeighbour
CNN=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestCNN=RandomForestClassifier(random_state=2024)
ForestCNN_PR,accuracyCNN,ForestCNN_ROC,roc_auc_CNN,Forestcnn=RunTrainModelCV(Resmaple=CNN,Model=ForestCNN,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 0.8181818181818182, 0.8888888888888888, 1.0, 1.0, 1.0, 0.8, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.875, 1.0, 0.8888888888888888, 0.7272727272727273, 0.6363636363636364, 0.8181818181818182, 0.7272727272727273, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.8235294117647058, 0.9333333333333333, 0.9, 0.8888888888888888, 0.8421052631578948, 0.7777777777777778, 0.9, 0.761904761904762, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.896551724137931, 0.9655172413793104, 0.9310344827586207, 0.9285714285714286, 0.8928571428571429, 0.8571428571428571, 0.9285714285714286, 0.8214285714285714, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9507070707070706,Recall:0.7683585858585859,F1_Score:0.8406486805248414,Accuracy:0.8935960591133005
模型测试集Precision:0.782608695652174,Recall:0.8181818181818182,F1_score:0.8,Accuracy:0.9906444906444907
-------------------测试集混淆举证-------------------
In [26]:
#输出PR和ROC曲线数据
ForestCNN_PR.to_excel('Data/PR Curve/Resample/ForestCNN_PR.xlsx')
ForestCNN_ROC.to_excel('Data/ROC Curve/Resample/ForestCNN_ROC.xlsx')
In [27]:
#使用IHT算法进行欠采样
from imblearn.under_sampling import InstanceHardnessThreshold
IHT=InstanceHardnessThreshold(random_state=2024,cv=5,n_jobs=-1)
ForestIHT=RandomForestClassifier(random_state=2024)
ForestIHT_PR,accuracyIHT,ForestIHT_ROC,roc_auc_IHT,ForestIHT=RunTrainModelCV(Resmaple=IHT,Model=ForestIHT,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 2265, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.3333333333333333, 0.7142857142857143, 0.45454545454545453, 0.8333333333333334, 0.5, 0.375, 0.5384615384615384, 0.7142857142857143, 0.38461538461538464, 0.6363636363636364]
Model valid train F1 Score of flod: [0.5, 0.8333333333333333, 0.625, 0.9090909090909091, 0.6666666666666666, 0.5454545454545454, 0.7000000000000001, 0.8333333333333333, 0.5555555555555556, 0.7777777777777778]
Model valid train Accuracy of flod: [0.9831223628691983, 0.9915611814345991, 0.9746835443037974, 0.9915611814345991, 0.9831223628691983, 0.9789029535864979, 0.9746835443037974, 0.9831223628691983, 0.9661016949152542, 0.9830508474576272]
模型Valid集Precision:1.0,Recall:0.548422410922411,F1_Score:0.6946212121212121,Accuracy:0.9809912036043766
模型测试集Precision:0.7333333333333333,Recall:0.5,F1_score:0.5945945945945945,Accuracy:0.9844074844074844
-------------------测试集混淆举证-------------------
In [28]:
#计算PR和ROC曲线
ForestIHT_PR.to_excel('Data/PR Curve/Resample/ForestIHT_PR.xlsx')
ForestIHT_ROC.to_excel('Data/ROC Curve/Resample/ForestIHT_ROC.xlsx')
In [29]:
#使用NearMiss进行欠采样
from imblearn.under_sampling import NearMiss
NM=NearMiss(sampling_strategy='not minority',n_jobs=-1)
ForestNM=RandomForestClassifier(random_state=2024)
ForestNM_PR,accuracyNM,ForestNM_ROC,roc_auc_NM,ForestNM=RunTrainModelCV(Resmaple=NM,Model=ForestNM,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 0.9285714285714286, 1.0, 0.9, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.8888888888888888, 1.0, 0.9333333333333333, 0.9285714285714286, 0.75, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train F1 Score of flod: [0.9411764705882353, 1.0, 0.9655172413793104, 0.9285714285714286, 0.8571428571428571, 0.9473684210526316, 1.0, 1.0, 1.0, 1.0]
Model valid train Accuracy of flod: [0.9523809523809523, 1.0, 0.9523809523809523, 0.9047619047619048, 0.9047619047619048, 0.9523809523809523, 1.0, 1.0, 1.0, 1.0]
模型Valid集Precision:0.9828571428571429,Recall:0.950079365079365,F1_Score:0.9639776418734464,Accuracy:0.9666666666666666
模型测试集Precision:0.037374658158614404,Recall:0.9318181818181818,F1_score:0.07186678352322523,Accuracy:0.4495841995841996
-------------------测试集混淆举证-------------------
In [30]:
#计算PR和ROC曲线
ForestNM_PR.to_excel('Data/PR Curve/Resample/ForestNM_PR.xlsx')
ForestNM_ROC.to_excel('Data/ROC Curve/Resample/ForestNM_ROC.xlsx')
In [31]:
#使用NeighbourhoodCleaningRule进行欠采样
from imblearn.under_sampling import NeighbourhoodCleaningRule
NBC=NeighbourhoodCleaningRule(sampling_strategy='not minority',n_jobs=-1)
ForestNBC=RandomForestClassifier(random_state=2024)
ForestNBC_PR,accuracyNBC,ForestNBC_ROC,roc_auc_NBC,ForestNBC=RunTrainModelCV(Resmaple=NBC,Model=ForestNBC,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 4325, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.16666666666666666, 0.0, 0.0, 0.2222222222222222, 0.0, 0.0625, 0.09090909090909091, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.2857142857142857, 0.0, 0.0, 0.3636363636363636, 0.0, 0.11764705882352941, 0.16666666666666669, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9729119638826185, 0.9887133182844243, 0.9661399548532731, 0.981941309255079, 0.9841986455981941, 0.9796839729119639, 0.9661399548532731, 0.9774266365688488, 0.9796380090497737, 0.9819004524886877]
模型Valid集Precision:0.4,Recall:0.054229797979797975,F1_Score:0.09336643748408455,Accuracy:0.9778694217746138
模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792
-------------------测试集混淆举证-------------------
In [32]:
#计算PR和ROC曲线
ForestNBC_PR.to_excel('Data/PR Curve/Resample/ForestNBC_PR.xlsx')
ForestNBC_ROC.to_excel('Data/ROC Curve/Resample/ForestNBC_ROC.xlsx')
In [33]:
#使用OneSidedSelection进行欠采样
from imblearn.under_sampling import OneSidedSelection
OSS=OneSidedSelection(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestOSS=RandomForestClassifier(random_state=2024)
ForestOSS_PR,accuracyOSS,ForestOSS_ROC,roc_auc_OSS,ForestOSS=RunTrainModelCV(Resmaple=OSS,Model=ForestOSS,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 4345, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.25, 0.13333333333333333, 0.15384615384615385, 0.15384615384615385, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.4, 0.23529411764705882, 0.2666666666666667, 0.2666666666666667, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9752808988764045, 0.9865168539325843, 0.9707865168539326, 0.9752808988764045, 0.9752808988764045, 0.9887640449438202, 0.9775280898876404, 0.9730337078651685, 0.9864864864864865, 0.9774774774774775]
模型Valid集Precision:0.4,Recall:0.0691025641025641,F1_Score:0.11686274509803922,Accuracy:0.9786435874076321
模型测试集Precision:1.0,Recall:0.06818181818181818,F1_score:0.1276595744680851,Accuracy:0.9786902286902287
-------------------测试集混淆举证-------------------
In [34]:
#计算PR和ROC曲线
ForestOSS_PR.to_excel('Data/PR Curve/Resample/ForestOSS_PR.xlsx')
ForestOSS_ROC.to_excel('Data/ROC Curve/Resample/ForestOSS_ROC.xlsx')
In [35]:
#使用随机欠采样进行数据欠采样
from imblearn.under_sampling import RandomUnderSampler
RUS=RandomUnderSampler(sampling_strategy='not minority',random_state=2024)
ForestRUS=RandomForestClassifier(random_state=2024)
ForestRUS_PR,accuracyRUS,ForestRUS_ROC,roc_auc_RUS,ForestRUS=RunTrainModelCV(Resmaple=RUS,Model=ForestRUS,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.9, 0.8181818181818182, 1.0, 1.0, 0.8, 1.0, 1.0, 0.9, 1.0, 1.0]
Model valid train Recall of flod: [1.0, 0.9, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888]
Model valid train F1 Score of flod: [0.9473684210526316, 0.8571428571428572, 0.9655172413793104, 1.0, 0.888888888888889, 1.0, 1.0, 0.9473684210526316, 1.0, 0.9411764705882353]
Model valid train Accuracy of flod: [0.9523809523809523, 0.8571428571428571, 0.9523809523809523, 1.0, 0.9047619047619048, 1.0, 1.0, 0.95, 1.0, 0.95]
模型Valid集Precision:0.9418181818181818,Recall:0.9722222222222221,F1_Score:0.9547462300104556,Accuracy:0.9566666666666667
模型测试集Precision:0.2679738562091503,Recall:0.9318181818181818,F1_score:0.416243654822335,Accuracy:0.9402286902286903
-------------------测试集混淆举证-------------------
In [36]:
#计算PR和ROC曲线
ForestRUS_PR.to_excel('Data/PR Curve/Resample/ForestRUS_PR.xlsx')
ForestRUS_ROC.to_excel('Data/ROC Curve/Resample/ForestRUS_ROC.xlsx')
In [37]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(Forest_PR['recall'], Forest_PR['precision'],color='red',label='RandomForest=%f'%Forest_Accuracy)
PR_ax.plot(ForestCNN_PR['recall'], ForestCNN_PR['precision'],color='green',label='CNN=%f'%accuracyCNN)
PR_ax.plot(ForestIHT_PR['recall'], ForestIHT_PR['precision'],color='blue',label='IHT=%f'%accuracyIHT)
PR_ax.plot(ForestNBC_PR['recall'], ForestNBC_PR['precision'],color='yellow',label='NBC=%f'%accuracyNBC)
PR_ax.plot(ForestNM_PR['recall'], ForestNM_PR['precision'],color='purple',label='NM=%f'%accuracyNM)
PR_ax.plot(ForestOSS_PR['recall'], ForestOSS_PR['precision'],color='cyan',label='OSS=%f'%accuracyOSS)
PR_ax.plot(ForestRUS_PR['recall'],ForestRUS_PR['precision'],color='pink',label='RUS=%f'%accuracyRUS)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')  
PR_ax.set_ylabel('Precision')    
plt.legend(loc="best")  
plt.show()
In [38]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(Forest_ROC['fpr'], Forest_ROC['tpr'],color='red',label='RandomForest=%f'%Forest_ROC_AUC)
ROC_ax.plot(ForestCNN_ROC['fpr'], ForestCNN_ROC['tpr'],color='green',label='CNN=%f'%roc_auc_CNN)
ROC_ax.plot(ForestIHT_ROC['fpr'], ForestIHT_ROC['tpr'],color='blue',label='IHT=%f'%roc_auc_IHT)
ROC_ax.plot(ForestNBC_ROC['fpr'], ForestNBC_ROC['tpr'],color='yellow',label='NBC=%f'%roc_auc_NBC)
ROC_ax.plot(ForestNM_ROC['fpr'], ForestNM_ROC['tpr'],color='purple',label='NM=%f'%roc_auc_NM)
ROC_ax.plot(ForestOSS_ROC['fpr'], ForestOSS_ROC['tpr'],color='cyan',label='OSS=%f'%roc_auc_OSS)
ROC_ax.plot(ForestRUS_ROC['fpr'],ForestRUS_ROC['tpr'],color='pink',label='RUS=%f'%roc_auc_RUS)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])  
ROC_ax.set_ylim([0, 1.05])  
ROC_ax.set_xlabel('FPR')  
ROC_ax.set_ylabel('TPR')    
plt.legend(loc="best")  
plt.show()
In [39]:
import joblib
Model=[Forest,ForestCNN,ForestIHT,ForestNM,ForestNBC,ForestOSS,ForestRUS]
ModelStr=['Forest.pkl','ForestCNN.pkl','ForestIHT.pkl','ForestNM.pkl','ForestNBC.pkl','ForestOSS.pkl','ForestRUS.pkl']
try:
    for model,modelstr in zip(Model,ModelStr):
        joblib.dump(model,'ML Models/Resample/'+modelstr)
    print('模型保存成功!')
except:
    print('模型保存异常!!!')
模型保存成功!

选择CNN作为数据重采样算法

In [40]:
ReData=MLData.iloc[:,:-1]    #提取数据集
ReLabel=MLData.iloc[:,-1]    #提取数据标签
print(ReData.shape)
print(ReLabel.shape) 
(6413, 72)
(6413,)

预处理数据:数据缩放、描述性统计学

In [41]:
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ReMLData_name=ReData.columns
MLStand=StandardScaler()    #数据标准化
StandData=MLStand.fit_transform(ReData)
MinMaxData=MinMaxScaler(feature_range=(0,1))    #数据缩放
MinMaxdata=MinMaxData.fit_transform(StandData)
MLRunData=pd.DataFrame(MinMaxdata)
MLRunData.columns=ReMLData_name
MLRunData.head(10)
Out[41]:
cg03706273 cg00412368 cg01370179 cg01522249 cg01999212 cg03718079 cg03977822 cg05100261 cg05876899 cg06104510 ... cg00941229 cg00958409 cg00962755 cg01077609 cg01101221 cg01446576 cg01550055 cg01717973 age gender_encode
0 0.366703 0.699228 0.627539 0.339860 0.725793 0.719028 0.610471 0.692839 0.495571 0.842822 ... 0.725793 0.715924 0.372852 0.399866 0.645909 0.720955 0.598192 0.541884 0.684211 0.0
1 0.463465 0.592648 0.638988 0.312063 0.502166 0.564485 0.531935 0.435958 0.520210 0.606743 ... 0.583368 0.699491 0.360245 0.375090 0.543283 0.593423 0.671892 0.468489 0.456140 1.0
2 0.419803 0.546794 0.629799 0.435326 0.553452 0.589086 0.544620 0.353111 0.507977 0.627347 ... 0.522001 0.742118 0.410469 0.375090 0.603949 0.560104 0.608857 0.346457 0.807018 1.0
3 0.316703 0.674653 0.606416 0.256460 0.675869 0.637623 0.701250 0.730939 0.531910 0.750907 ... 0.566385 0.722588 0.310634 0.399866 0.567367 0.502166 0.560468 0.442870 0.763158 0.0
4 0.345865 0.506133 0.618676 0.441581 0.619161 0.521338 0.609608 0.410436 0.520210 0.644800 ... 0.583368 0.740716 0.404357 0.349814 0.549814 0.538830 0.567359 0.459086 0.763158 1.0
5 0.435973 0.561169 0.624070 0.419851 0.703336 0.519129 0.620814 0.421574 0.527038 0.624162 ... 0.477988 0.757873 0.433043 0.382025 0.543283 0.553452 0.670629 0.448602 0.359649 1.0
6 0.402192 0.691920 0.663395 0.306385 0.693490 0.621787 0.672487 0.760064 0.511868 0.839015 ... 0.553452 0.705214 0.350654 0.295393 0.481720 0.502166 0.666816 0.472852 0.684211 0.0
7 0.371111 0.542279 0.628108 0.312063 0.522001 0.462208 0.563362 0.364092 0.524701 0.624162 ... 0.619161 0.701428 0.465032 0.359144 0.582520 0.530733 0.655144 0.453995 0.701754 1.0
8 0.441267 0.564934 0.635818 0.479486 0.583368 0.582309 0.575699 0.307450 0.518048 0.590573 ... 0.615253 0.712455 0.357063 0.388415 0.595861 0.538830 0.691576 0.430188 0.605263 1.0
9 0.470881 0.569757 0.641060 0.471262 0.598135 0.584618 0.625118 0.307450 0.545459 0.560855 ... 0.649430 0.724201 0.348719 0.382025 0.561819 0.602660 0.731723 0.468489 0.657895 1.0

10 rows × 72 columns

使用CNN算法进行预处理

In [42]:
from imblearn.under_sampling import CondensedNearestNeighbour
CNNML=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
In [43]:
#测试Logistic回归
from sklearn.linear_model import LogisticRegression
Logistic=LogisticRegression(penalty="l2",dual=True,tol=1e-4,C=1.0,fit_intercept=True,random_state=2024,solver='liblinear',
                            max_iter=100,multi_class="auto",verbose=0, warm_start=False,n_jobs=-1,l1_ratio=None)
Logistic_PR,Logistic_Accuracy,Logistic_ROC,Logistic_AUC,Logistic_ML=RunTrainModelCV(Resmaple=CNNML,Model=Logistic,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9365079365079365, 0.8970588235294118, 0.9130434782608695, 0.9130434782608695, 0.921875, 0.9333333333333333, 0.9508196721311475, 0.9230769230769231, 0.9344262295081968, 0.9142857142857143]
Model train Recall of flod: [0.6344086021505376, 0.6421052631578947, 0.6702127659574468, 0.6702127659574468, 0.6413043478260869, 0.6086956521739131, 0.6304347826086957, 0.6521739130434783, 0.6263736263736264, 0.6956521739130435]
Model train F1 Score of flod: [0.7564102564102564, 0.7484662576687117, 0.7730061349693251, 0.7730061349693251, 0.7564102564102563, 0.7368421052631579, 0.758169934640523, 0.7643312101910829, 0.7500000000000001, 0.7901234567901234]
Model train Accuracy of flod: [0.8503937007874016, 0.8385826771653543, 0.8543307086614174, 0.8549019607843137, 0.8509803921568627, 0.8431372549019608, 0.8549019607843137, 0.8549019607843137, 0.8509803921568627, 0.8666666666666667]
模型训练集Precision:0.9237470588894403,Recall:0.6471573893162169,F1_Score:0.7606765747312763,Accuracy:0.8519777674849467
Model valid Precision of flod: [1.0, 0.8888888888888888, 0.7142857142857143, 1.0, 0.8333333333333334, 1.0, 0.7777777777777778, 0.8888888888888888, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.5555555555555556, 0.6666666666666666, 0.45454545454545453, 0.7272727272727273, 0.6363636363636364, 0.7272727272727273, 0.5, 0.45454545454545453]
Model valid train F1 Score of flod: [0.5714285714285715, 0.9411764705882353, 0.6250000000000001, 0.8, 0.5882352941176471, 0.8421052631578948, 0.7000000000000001, 0.7999999999999999, 0.6666666666666666, 0.625]
Model valid train Accuracy of flod: [0.7931034482758621, 0.9655172413793104, 0.7931034482758621, 0.8928571428571429, 0.75, 0.8928571428571429, 0.7857142857142857, 0.8571428571428571, 0.7857142857142857, 0.7857142857142857]
模型Valid集Precision:0.9103174603174603,Recall:0.6122222222222222,F1_Score:0.7159612265959016,Accuracy:0.8301724137931034
模型测试集Precision:0.8064516129032258,Recall:0.5681818181818182,F1_score:0.6666666666666667,Accuracy:0.987006237006237
-------------------测试集混淆举证-------------------
In [44]:
#输出PR曲线和ROC曲线结果
Logistic_PR.to_excel('Data/PR Curve/ML/Logistic_PR.xlsx','UTF-8')
Logistic_ROC.to_excel('Data/ROC Curve/ML/Logistic_ROC.xlsx','UTF-8')
In [45]:
#测试SVM算法
from sklearn.svm import SVC
SVM_Classifier=SVC(C=1.0,kernel="rbf",degree=3,gamma="scale",coef0=0.0,shrinking=True,probability=True,tol=1e-3, 
                   cache_size=200,max_iter=-1, decision_function_shape="ovr",random_state=2024)
SVM_PR,SVM_Accuracy,SVM_ROC,SVM_AUC,SVM_ML=RunTrainModelCV(Resmaple=CNNML,Model=SVM_Classifier,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9473684210526315, 0.9487179487179487, 0.9285714285714286, 0.9302325581395349, 0.927710843373494, 0.935064935064935, 0.9466666666666667, 0.9397590361445783, 0.9333333333333333, 0.9156626506024096]
Model train Recall of flod: [0.7741935483870968, 0.7789473684210526, 0.8297872340425532, 0.851063829787234, 0.8369565217391305, 0.782608695652174, 0.7717391304347826, 0.8478260869565217, 0.7692307692307693, 0.8260869565217391]
Model train F1 Score of flod: [0.8520710059171597, 0.8554913294797688, 0.8764044943820225, 0.888888888888889, 0.8800000000000001, 0.8520710059171598, 0.8502994011976047, 0.8914285714285715, 0.8433734939759036, 0.8685714285714285]
Model train Accuracy of flod: [0.9015748031496063, 0.9015748031496063, 0.9133858267716536, 0.9215686274509803, 0.9176470588235294, 0.9019607843137255, 0.9019607843137255, 0.9254901960784314, 0.8980392156862745, 0.9098039215686274]
模型训练集Precision:0.9353087821666962,Recall:0.8068440141173052,F1_Score:0.8658599619758508,Accuracy:0.9093006021306161
Model valid Precision of flod: [0.8, 0.8888888888888888, 0.875, 0.7777777777777778, 1.0, 1.0, 0.9, 0.8181818181818182, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.7777777777777778, 0.7777777777777778, 0.5454545454545454, 0.6363636363636364, 0.8181818181818182, 0.8181818181818182, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.5333333333333333, 0.9411764705882353, 0.823529411764706, 0.7777777777777778, 0.7058823529411764, 0.7777777777777778, 0.8571428571428572, 0.8181818181818182, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.7586206896551724, 0.9655172413793104, 0.896551724137931, 0.8571428571428571, 0.8214285714285714, 0.8571428571428571, 0.8928571428571429, 0.8571428571428571, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9059848484848485,Recall:0.7084343434343434,F1_Score:0.7813749167928734,Accuracy:0.8620689655172413
模型测试集Precision:0.85,Recall:0.7727272727272727,F1_score:0.8095238095238095,Accuracy:0.9916839916839917
-------------------测试集混淆举证-------------------
In [46]:
#输出PR曲线和ROC曲线结果
SVM_PR.to_excel('Data/PR Curve/ML/SVM_PR.xlsx','UTF-8')
SVM_ROC.to_excel('Data/ROC Curve/ML/SVM_ROC.xlsx','UTF-8')
In [47]:
#测试决策树算法
import numpy as np
from sklearn.tree import DecisionTreeClassifier
DecisionTree=DecisionTreeClassifier(criterion='entropy',splitter='best',max_depth=7,min_samples_split=6,random_state=2024)
DTC_PR,DTC_Accuracy,DTC_ROC,DTC_AUC,DTC_ML=RunTrainModelCV(Resmaple=CNNML,Model=DecisionTree,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.989010989010989, 0.92, 0.9891304347826086, 0.9893617021276596, 0.9578947368421052, 0.9479166666666666, 0.9278350515463918, 0.989247311827957, 0.9574468085106383, 0.9574468085106383]
Model train Recall of flod: [0.967741935483871, 0.968421052631579, 0.9680851063829787, 0.9893617021276596, 0.9891304347826086, 0.9891304347826086, 0.9782608695652174, 1.0, 0.989010989010989, 0.9782608695652174]
Model train F1 Score of flod: [0.9782608695652174, 0.9435897435897437, 0.9784946236559139, 0.9893617021276596, 0.9732620320855614, 0.9680851063829786, 0.9523809523809524, 0.9945945945945946, 0.972972972972973, 0.967741935483871]
Model train Accuracy of flod: [0.984251968503937, 0.9566929133858267, 0.984251968503937, 0.9921568627450981, 0.9803921568627451, 0.9764705882352941, 0.9647058823529412, 0.996078431372549, 0.9803921568627451, 0.9764705882352941]
模型训练集Precision:0.9625290509825655,Recall:0.9817403394332729,F1_Score:0.9718744532839466,Accuracy:0.9791863517060367
Model valid Precision of flod: [0.8571428571428571, 0.6, 0.6363636363636364, 0.5, 0.8888888888888888, 0.8888888888888888, 0.75, 0.7272727272727273, 0.8888888888888888, 0.7272727272727273]
Model valid train Recall of flod: [0.6, 0.75, 0.7777777777777778, 0.4444444444444444, 0.7272727272727273, 0.7272727272727273, 0.5454545454545454, 0.7272727272727273, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.6666666666666665, 0.7000000000000001, 0.47058823529411764, 0.7999999999999999, 0.7999999999999999, 0.631578947368421, 0.7272727272727273, 0.761904761904762, 0.7272727272727273]
Model valid train Accuracy of flod: [0.8275862068965517, 0.7931034482758621, 0.7931034482758621, 0.6785714285714286, 0.8571428571428571, 0.8571428571428571, 0.75, 0.7857142857142857, 0.8214285714285714, 0.7857142857142857]
模型Valid集Precision:0.7464718614718614,Recall:0.6693434343434344,F1_Score:0.6991166418720598,Accuracy:0.7949507389162561
模型测试集Precision:0.1336206896551724,Recall:0.7045454545454546,F1_score:0.2246376811594203,Accuracy:0.8887733887733887
-------------------测试集混淆举证-------------------
In [48]:
#输出PR曲线和ROC曲线结果
DTC_PR.to_excel('Data/PR Curve/ML/DTC_PR.xlsx','UTF-8')
DTC_ROC.to_excel('Data/ROC Curve/ML/DTC_ROC.xlsx','UTF-8')
In [49]:
#对随机森林进行网格搜索
from sklearn.ensemble import RandomForestClassifier
RandomForest=RandomForestClassifier(n_estimators=27,criterion='entropy',max_depth=7,min_samples_split=6,
                                    bootstrap=True,min_samples_leaf=9,max_features='sqrt',random_state=2024)
RondomForest_PR,RondomForest_Accuracy,RondomForest_ROC,RondomForest_AUC,Forest_ML=RunTrainModelCV(Resmaple=CNNML,Model=RandomForest,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9887640449438202, 1.0, 0.9886363636363636, 1.0, 0.9886363636363636, 1.0, 1.0, 0.967032967032967, 1.0, 0.9882352941176471]
Model train Recall of flod: [0.946236559139785, 0.9368421052631579, 0.925531914893617, 0.9787234042553191, 0.9456521739130435, 0.967391304347826, 0.9347826086956522, 0.9565217391304348, 0.9560439560439561, 0.9130434782608695]
Model train F1 Score of flod: [0.967032967032967, 0.967391304347826, 0.9560439560439561, 0.989247311827957, 0.9666666666666666, 0.9834254143646408, 0.9662921348314606, 0.9617486338797815, 0.9775280898876404, 0.9491525423728814]
Model train Accuracy of flod: [0.9763779527559056, 0.9763779527559056, 0.968503937007874, 0.9921568627450981, 0.9764705882352941, 0.9882352941176471, 0.9764705882352941, 0.9725490196078431, 0.984313725490196, 0.9647058823529412]
模型训练集Precision:0.9921305033367162,Recall:0.9460769243943661,F1_Score:0.9684529021255777,Accuracy:0.9776161803304
Model valid Precision of flod: [1.0, 0.75, 0.7272727272727273, 0.9, 1.0, 1.0, 1.0, 0.8571428571428571, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.75, 0.8888888888888888, 1.0, 0.5454545454545454, 0.5454545454545454, 0.8181818181818182, 0.5454545454545454, 0.6666666666666666, 0.6363636363636364]
Model valid train F1 Score of flod: [0.8235294117647058, 0.75, 0.7999999999999999, 0.9473684210526316, 0.7058823529411764, 0.7058823529411764, 0.9, 0.6666666666666665, 0.8, 0.7777777777777778]
Model valid train Accuracy of flod: [0.896551724137931, 0.8620689655172413, 0.8620689655172413, 0.9642857142857143, 0.8214285714285714, 0.8214285714285714, 0.9285714285714286, 0.7857142857142857, 0.8571428571428571, 0.8571428571428571]
模型Valid集Precision:0.9234415584415585,Recall:0.7096464646464647,F1_Score:0.7877106983144134,Accuracy:0.8656403940886699
模型测试集Precision:0.6415094339622641,Recall:0.7727272727272727,F1_score:0.7010309278350515,Accuracy:0.9849272349272349
-------------------测试集混淆举证-------------------
In [50]:
#输出PR曲线和ROC曲线结果
RondomForest_PR.to_excel('Data/PR Curve/ML/RondomForest_PR.xlsx','UTF-8')
RondomForest_ROC.to_excel('Data/ROC Curve/ML/RondomForest_ROC.xlsx','UTF-8')
In [51]:
#测试XGBoost模型
from xgboost import XGBClassifier
XGBoost=XGBClassifier(n_jobs=-1,verbosity=1,tree_method='auto',gpu_id=0,random_state=2024)
XGBoost_PR,XGBoost_Accuracy,XGBoost_ROC,XGBoost_AUC,XGBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=XGBoost,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.8571428571428571, 0.7, 0.75, 0.8, 0.9, 1.0, 1.0, 0.6923076923076923, 0.8888888888888888, 0.8]
Model valid train Recall of flod: [0.6, 0.875, 1.0, 0.8888888888888888, 0.8181818181818182, 0.7272727272727273, 0.7272727272727273, 0.8181818181818182, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.7777777777777777, 0.8571428571428571, 0.8421052631578948, 0.8571428571428572, 0.8421052631578948, 0.8421052631578948, 0.7500000000000001, 0.761904761904762, 0.761904761904762]
Model valid train Accuracy of flod: [0.8275862068965517, 0.8620689655172413, 0.896551724137931, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.7857142857142857, 0.8214285714285714, 0.8214285714285714]
模型Valid集Precision:0.8388339438339438,Recall:0.7848737373737374,F1_Score:0.7998071158287876,Accuracy:0.8586206896551725
模型测试集Precision:0.42528735632183906,Recall:0.8409090909090909,F1_score:0.564885496183206,Accuracy:0.9703742203742204
-------------------测试集混淆举证-------------------
In [52]:
#输出PR曲线和ROC曲线结果
XGBoost_PR.to_excel('Data/PR Curve/ML/XGBoost_PR.xlsx','UTF-8')
XGBoost_ROC.to_excel('Data/ROC Curve/ML/XGBoost_ROC.xlsx','UTF-8')
In [53]:
#测试LightGBM算法
from lightgbm import LGBMClassifier
LightGBM=LGBMClassifier(boosting_type='gbdt',num_leaves=31,learning_rate=0.01,n_estimators=200,
                        n_jobs=-1,objective='binary',metric='binary_logloss',keep_training_booster=True,
                       importance_type='gini',random_state=2024)
LightGBM_PR,LightGBM_Accuracy,LightGBM_ROC,LightGBM_AUC,LightGBM_ML=RunTrainModelCV(Resmaple=CNNML,Model=LightGBM,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 93, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.366142 -> initscore=-0.548805
[LightGBM] [Info] Start training from score -0.548805
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 95, number of negative: 159
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4236
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.374016 -> initscore=-0.515027
[LightGBM] [Info] Start training from score -0.515027
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 160
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.370079 -> initscore=-0.531879
[LightGBM] [Info] Start training from score -0.531879
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4268
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.368627 -> initscore=-0.538110
[LightGBM] [Info] Start training from score -0.538110
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4303
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4298
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4245
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4269
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 91, number of negative: 164
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4277
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.356863 -> initscore=-0.589007
[LightGBM] [Info] Start training from score -0.589007
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4292
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
Model train Precision of flod: [1.0, 1.0, 1.0, 0.9893617021276596, 0.989010989010989, 1.0, 0.9888888888888889, 1.0, 1.0, 1.0]
Model train Recall of flod: [0.978494623655914, 0.9894736842105263, 1.0, 0.9893617021276596, 0.9782608695652174, 1.0, 0.967391304347826, 0.9891304347826086, 0.978021978021978, 0.9891304347826086]
Model train F1 Score of flod: [0.9891304347826088, 0.9947089947089947, 1.0, 0.9893617021276596, 0.9836065573770493, 1.0, 0.978021978021978, 0.994535519125683, 0.9888888888888888, 0.994535519125683]
Model train Accuracy of flod: [0.9921259842519685, 0.9960629921259843, 1.0, 0.9921568627450981, 0.9882352941176471, 1.0, 0.984313725490196, 0.996078431372549, 0.9921568627450981, 0.996078431372549]
模型训练集Precision:0.9967261580027538,Recall:0.9859265031494339,F1_Score:0.9912789594158546,Accuracy:0.9937208584221089
Model valid Precision of flod: [1.0, 0.7777777777777778, 0.8, 0.6, 0.875, 1.0, 1.0, 0.7, 0.8888888888888888, 0.8333333333333334]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.6666666666666666, 0.6363636363636364, 0.7272727272727273, 0.7272727272727273, 0.6363636363636364, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.8235294117647058, 0.823529411764706, 0.8421052631578948, 0.631578947368421, 0.7368421052631579, 0.8421052631578948, 0.8421052631578948, 0.6666666666666666, 0.761904761904762, 0.8695652173913043]
Model valid train Accuracy of flod: [0.896551724137931, 0.896551724137931, 0.896551724137931, 0.75, 0.8214285714285714, 0.8928571428571429, 0.8928571428571429, 0.75, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.8475000000000001,Recall:0.7433585858585859,F1_Score:0.7839932311597407,Accuracy:0.8511083743842365
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 103, number of negative: 180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4619
[LightGBM] [Info] Number of data points in the train set: 283, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.363958 -> initscore=-0.558228
[LightGBM] [Info] Start training from score -0.558228
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
模型测试集Precision:0.47297297297297297,Recall:0.7954545454545454,F1_score:0.5932203389830508,Accuracy:0.975051975051975
-------------------测试集混淆举证-------------------
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
In [54]:
#输出PR曲线和ROC曲线结果
LightGBM_PR.to_excel('Data/PR Curve/ML/LightGBM_PR.xlsx','UTF-8')
LightGBM_ROC.to_excel('Data/ROC Curve/ML/LightGBM_ROC.xlsx','UTF-8')
In [55]:
#测试CatBoost算法
from catboost import CatBoostClassifier
CatBoost=CatBoostClassifier(learning_rate=0.01,depth=6,iterations=500,thread_count=-1)
CatBoost_PR,CatBoost_Accuracy,CatBoost_ROC,CatBoost_AUC,CatBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=CatBoost,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
0:	learn: 0.6890172	total: 49.9ms	remaining: 24.9s
1:	learn: 0.6821106	total: 52.3ms	remaining: 13s
2:	learn: 0.6753477	total: 54.7ms	remaining: 9.06s
3:	learn: 0.6707910	total: 57.1ms	remaining: 7.08s
4:	learn: 0.6652468	total: 59.3ms	remaining: 5.87s
5:	learn: 0.6592446	total: 61.6ms	remaining: 5.07s
6:	learn: 0.6543080	total: 64.1ms	remaining: 4.52s
7:	learn: 0.6488765	total: 66.5ms	remaining: 4.09s
8:	learn: 0.6434927	total: 68.8ms	remaining: 3.75s
9:	learn: 0.6392815	total: 71ms	remaining: 3.48s
10:	learn: 0.6325844	total: 73.2ms	remaining: 3.25s
11:	learn: 0.6274175	total: 75.4ms	remaining: 3.07s
12:	learn: 0.6247561	total: 78.5ms	remaining: 2.94s
13:	learn: 0.6193241	total: 80.9ms	remaining: 2.81s
14:	learn: 0.6149203	total: 83.2ms	remaining: 2.69s
15:	learn: 0.6097759	total: 85.3ms	remaining: 2.58s
16:	learn: 0.6057541	total: 87.5ms	remaining: 2.49s
17:	learn: 0.5991016	total: 89.7ms	remaining: 2.4s
18:	learn: 0.5946312	total: 91.8ms	remaining: 2.32s
19:	learn: 0.5900550	total: 94ms	remaining: 2.26s
20:	learn: 0.5848866	total: 96.1ms	remaining: 2.19s
21:	learn: 0.5806826	total: 98.3ms	remaining: 2.13s
22:	learn: 0.5768614	total: 101ms	remaining: 2.08s
23:	learn: 0.5719233	total: 103ms	remaining: 2.04s
24:	learn: 0.5684533	total: 105ms	remaining: 2s
25:	learn: 0.5644880	total: 107ms	remaining: 1.95s
26:	learn: 0.5599957	total: 109ms	remaining: 1.92s
27:	learn: 0.5560455	total: 112ms	remaining: 1.88s
28:	learn: 0.5516520	total: 114ms	remaining: 1.85s
29:	learn: 0.5468545	total: 116ms	remaining: 1.82s
30:	learn: 0.5434721	total: 118ms	remaining: 1.79s
31:	learn: 0.5399739	total: 121ms	remaining: 1.76s
32:	learn: 0.5349289	total: 123ms	remaining: 1.74s
33:	learn: 0.5311752	total: 125ms	remaining: 1.71s
34:	learn: 0.5272861	total: 127ms	remaining: 1.69s
35:	learn: 0.5229500	total: 129ms	remaining: 1.67s
36:	learn: 0.5186875	total: 132ms	remaining: 1.65s
37:	learn: 0.5152439	total: 134ms	remaining: 1.63s
38:	learn: 0.5125087	total: 136ms	remaining: 1.6s
39:	learn: 0.5090388	total: 138ms	remaining: 1.59s
40:	learn: 0.5051904	total: 140ms	remaining: 1.57s
41:	learn: 0.5016887	total: 142ms	remaining: 1.55s
42:	learn: 0.4986306	total: 145ms	remaining: 1.54s
43:	learn: 0.4949284	total: 147ms	remaining: 1.52s
44:	learn: 0.4909501	total: 149ms	remaining: 1.51s
45:	learn: 0.4877446	total: 151ms	remaining: 1.49s
46:	learn: 0.4840812	total: 154ms	remaining: 1.48s
47:	learn: 0.4793788	total: 156ms	remaining: 1.47s
48:	learn: 0.4761436	total: 158ms	remaining: 1.46s
49:	learn: 0.4720096	total: 160ms	remaining: 1.44s
50:	learn: 0.4685819	total: 164ms	remaining: 1.44s
51:	learn: 0.4659041	total: 166ms	remaining: 1.43s
52:	learn: 0.4633031	total: 169ms	remaining: 1.42s
53:	learn: 0.4600147	total: 171ms	remaining: 1.41s
54:	learn: 0.4567615	total: 173ms	remaining: 1.4s
55:	learn: 0.4543521	total: 176ms	remaining: 1.4s
56:	learn: 0.4515392	total: 178ms	remaining: 1.39s
57:	learn: 0.4484318	total: 181ms	remaining: 1.38s
58:	learn: 0.4452804	total: 183ms	remaining: 1.37s
59:	learn: 0.4423427	total: 185ms	remaining: 1.36s
60:	learn: 0.4396107	total: 188ms	remaining: 1.35s
61:	learn: 0.4365307	total: 190ms	remaining: 1.34s
62:	learn: 0.4324949	total: 193ms	remaining: 1.33s
63:	learn: 0.4291341	total: 195ms	remaining: 1.33s
64:	learn: 0.4258674	total: 197ms	remaining: 1.32s
65:	learn: 0.4228827	total: 199ms	remaining: 1.31s
66:	learn: 0.4204163	total: 202ms	remaining: 1.3s
67:	learn: 0.4176325	total: 204ms	remaining: 1.3s
68:	learn: 0.4154801	total: 206ms	remaining: 1.29s
69:	learn: 0.4126659	total: 208ms	remaining: 1.28s
70:	learn: 0.4105826	total: 211ms	remaining: 1.27s
71:	learn: 0.4082540	total: 213ms	remaining: 1.27s
72:	learn: 0.4061413	total: 215ms	remaining: 1.26s
73:	learn: 0.4035120	total: 218ms	remaining: 1.25s
74:	learn: 0.4005105	total: 220ms	remaining: 1.24s
75:	learn: 0.3974554	total: 222ms	remaining: 1.24s
76:	learn: 0.3953760	total: 224ms	remaining: 1.23s
77:	learn: 0.3931199	total: 226ms	remaining: 1.23s
78:	learn: 0.3908388	total: 229ms	remaining: 1.22s
79:	learn: 0.3889399	total: 231ms	remaining: 1.21s
80:	learn: 0.3865093	total: 233ms	remaining: 1.21s
81:	learn: 0.3841485	total: 236ms	remaining: 1.2s
82:	learn: 0.3814131	total: 238ms	remaining: 1.19s
83:	learn: 0.3786283	total: 240ms	remaining: 1.19s
84:	learn: 0.3764786	total: 242ms	remaining: 1.18s
85:	learn: 0.3743559	total: 245ms	remaining: 1.18s
86:	learn: 0.3724375	total: 247ms	remaining: 1.17s
87:	learn: 0.3698552	total: 250ms	remaining: 1.17s
88:	learn: 0.3680379	total: 253ms	remaining: 1.17s
89:	learn: 0.3660191	total: 256ms	remaining: 1.16s
90:	learn: 0.3643221	total: 258ms	remaining: 1.16s
91:	learn: 0.3618923	total: 261ms	remaining: 1.16s
92:	learn: 0.3599345	total: 264ms	remaining: 1.16s
93:	learn: 0.3581246	total: 267ms	remaining: 1.15s
94:	learn: 0.3564155	total: 269ms	remaining: 1.15s
95:	learn: 0.3545144	total: 272ms	remaining: 1.14s
96:	learn: 0.3523735	total: 275ms	remaining: 1.14s
97:	learn: 0.3501672	total: 277ms	remaining: 1.14s
98:	learn: 0.3484675	total: 280ms	remaining: 1.13s
99:	learn: 0.3458134	total: 282ms	remaining: 1.13s
100:	learn: 0.3438340	total: 285ms	remaining: 1.13s
101:	learn: 0.3416282	total: 288ms	remaining: 1.12s
102:	learn: 0.3395181	total: 290ms	remaining: 1.12s
103:	learn: 0.3376651	total: 293ms	remaining: 1.11s
104:	learn: 0.3356077	total: 296ms	remaining: 1.11s
105:	learn: 0.3336075	total: 298ms	remaining: 1.11s
106:	learn: 0.3318092	total: 301ms	remaining: 1.1s
107:	learn: 0.3299776	total: 303ms	remaining: 1.1s
108:	learn: 0.3284238	total: 306ms	remaining: 1.1s
109:	learn: 0.3266575	total: 308ms	remaining: 1.09s
110:	learn: 0.3250016	total: 311ms	remaining: 1.09s
111:	learn: 0.3230045	total: 314ms	remaining: 1.09s
112:	learn: 0.3215742	total: 316ms	remaining: 1.08s
113:	learn: 0.3194088	total: 319ms	remaining: 1.08s
114:	learn: 0.3178187	total: 322ms	remaining: 1.08s
115:	learn: 0.3167521	total: 324ms	remaining: 1.07s
116:	learn: 0.3153649	total: 326ms	remaining: 1.07s
117:	learn: 0.3136586	total: 329ms	remaining: 1.06s
118:	learn: 0.3119299	total: 331ms	remaining: 1.06s
119:	learn: 0.3102071	total: 333ms	remaining: 1.05s
120:	learn: 0.3080839	total: 335ms	remaining: 1.05s
121:	learn: 0.3062139	total: 337ms	remaining: 1.04s
122:	learn: 0.3042707	total: 340ms	remaining: 1.04s
123:	learn: 0.3030945	total: 342ms	remaining: 1.04s
124:	learn: 0.3015155	total: 344ms	remaining: 1.03s
125:	learn: 0.3002482	total: 346ms	remaining: 1.03s
126:	learn: 0.2985638	total: 348ms	remaining: 1.02s
127:	learn: 0.2966712	total: 350ms	remaining: 1.02s
128:	learn: 0.2951537	total: 353ms	remaining: 1.01s
129:	learn: 0.2937992	total: 355ms	remaining: 1.01s
130:	learn: 0.2922054	total: 358ms	remaining: 1.01s
131:	learn: 0.2900515	total: 360ms	remaining: 1s
132:	learn: 0.2880529	total: 362ms	remaining: 999ms
133:	learn: 0.2866650	total: 364ms	remaining: 995ms
134:	learn: 0.2852373	total: 367ms	remaining: 991ms
135:	learn: 0.2840118	total: 369ms	remaining: 987ms
136:	learn: 0.2823647	total: 371ms	remaining: 983ms
137:	learn: 0.2806569	total: 373ms	remaining: 980ms
138:	learn: 0.2794713	total: 376ms	remaining: 976ms
139:	learn: 0.2780620	total: 378ms	remaining: 972ms
140:	learn: 0.2765585	total: 380ms	remaining: 967ms
141:	learn: 0.2752343	total: 382ms	remaining: 963ms
142:	learn: 0.2737898	total: 384ms	remaining: 960ms
143:	learn: 0.2724447	total: 387ms	remaining: 956ms
144:	learn: 0.2709554	total: 389ms	remaining: 952ms
145:	learn: 0.2696543	total: 391ms	remaining: 948ms
146:	learn: 0.2685485	total: 393ms	remaining: 944ms
147:	learn: 0.2669913	total: 395ms	remaining: 940ms
148:	learn: 0.2656380	total: 397ms	remaining: 936ms
149:	learn: 0.2644800	total: 399ms	remaining: 932ms
150:	learn: 0.2632481	total: 402ms	remaining: 928ms
151:	learn: 0.2618476	total: 404ms	remaining: 925ms
152:	learn: 0.2608755	total: 406ms	remaining: 921ms
153:	learn: 0.2596851	total: 408ms	remaining: 917ms
154:	learn: 0.2578798	total: 411ms	remaining: 914ms
155:	learn: 0.2567540	total: 413ms	remaining: 910ms
156:	learn: 0.2554965	total: 415ms	remaining: 906ms
157:	learn: 0.2542260	total: 417ms	remaining: 903ms
158:	learn: 0.2530898	total: 419ms	remaining: 899ms
159:	learn: 0.2518054	total: 421ms	remaining: 895ms
160:	learn: 0.2506140	total: 424ms	remaining: 892ms
161:	learn: 0.2495534	total: 426ms	remaining: 889ms
162:	learn: 0.2483117	total: 428ms	remaining: 885ms
163:	learn: 0.2465146	total: 430ms	remaining: 882ms
164:	learn: 0.2454966	total: 433ms	remaining: 878ms
165:	learn: 0.2439931	total: 435ms	remaining: 875ms
166:	learn: 0.2426780	total: 437ms	remaining: 871ms
167:	learn: 0.2411967	total: 439ms	remaining: 868ms
168:	learn: 0.2402197	total: 441ms	remaining: 864ms
169:	learn: 0.2391899	total: 443ms	remaining: 861ms
170:	learn: 0.2381740	total: 446ms	remaining: 857ms
171:	learn: 0.2372269	total: 448ms	remaining: 854ms
172:	learn: 0.2362036	total: 450ms	remaining: 850ms
173:	learn: 0.2354247	total: 452ms	remaining: 847ms
174:	learn: 0.2341622	total: 454ms	remaining: 843ms
175:	learn: 0.2326419	total: 456ms	remaining: 840ms
176:	learn: 0.2316699	total: 459ms	remaining: 837ms
177:	learn: 0.2303711	total: 461ms	remaining: 834ms
178:	learn: 0.2293186	total: 463ms	remaining: 831ms
179:	learn: 0.2278786	total: 465ms	remaining: 827ms
180:	learn: 0.2265008	total: 468ms	remaining: 824ms
181:	learn: 0.2252681	total: 470ms	remaining: 821ms
182:	learn: 0.2244853	total: 472ms	remaining: 817ms
183:	learn: 0.2233281	total: 474ms	remaining: 814ms
184:	learn: 0.2225159	total: 476ms	remaining: 811ms
185:	learn: 0.2216811	total: 478ms	remaining: 808ms
186:	learn: 0.2207573	total: 481ms	remaining: 804ms
187:	learn: 0.2197464	total: 483ms	remaining: 802ms
188:	learn: 0.2188808	total: 485ms	remaining: 798ms
189:	learn: 0.2181010	total: 487ms	remaining: 795ms
190:	learn: 0.2171762	total: 489ms	remaining: 792ms
191:	learn: 0.2162453	total: 492ms	remaining: 789ms
192:	learn: 0.2153847	total: 494ms	remaining: 785ms
193:	learn: 0.2144647	total: 496ms	remaining: 782ms
194:	learn: 0.2136304	total: 498ms	remaining: 779ms
195:	learn: 0.2123853	total: 500ms	remaining: 776ms
196:	learn: 0.2114077	total: 503ms	remaining: 773ms
197:	learn: 0.2105987	total: 505ms	remaining: 770ms
198:	learn: 0.2095145	total: 507ms	remaining: 767ms
199:	learn: 0.2086813	total: 509ms	remaining: 764ms
200:	learn: 0.2076565	total: 511ms	remaining: 761ms
201:	learn: 0.2066924	total: 515ms	remaining: 759ms
202:	learn: 0.2056270	total: 517ms	remaining: 756ms
203:	learn: 0.2046471	total: 519ms	remaining: 753ms
204:	learn: 0.2040298	total: 521ms	remaining: 750ms
205:	learn: 0.2030070	total: 524ms	remaining: 747ms
206:	learn: 0.2021029	total: 526ms	remaining: 744ms
207:	learn: 0.2013332	total: 528ms	remaining: 741ms
208:	learn: 0.2004660	total: 530ms	remaining: 738ms
209:	learn: 0.1995133	total: 532ms	remaining: 735ms
210:	learn: 0.1987373	total: 535ms	remaining: 732ms
211:	learn: 0.1977866	total: 537ms	remaining: 729ms
212:	learn: 0.1968224	total: 539ms	remaining: 726ms
213:	learn: 0.1962308	total: 541ms	remaining: 724ms
214:	learn: 0.1955591	total: 544ms	remaining: 721ms
215:	learn: 0.1949083	total: 546ms	remaining: 718ms
216:	learn: 0.1943323	total: 548ms	remaining: 715ms
217:	learn: 0.1934688	total: 551ms	remaining: 712ms
218:	learn: 0.1924126	total: 553ms	remaining: 709ms
219:	learn: 0.1916558	total: 555ms	remaining: 706ms
220:	learn: 0.1908868	total: 557ms	remaining: 703ms
221:	learn: 0.1899205	total: 559ms	remaining: 701ms
222:	learn: 0.1894475	total: 562ms	remaining: 698ms
223:	learn: 0.1889311	total: 564ms	remaining: 695ms
224:	learn: 0.1878313	total: 566ms	remaining: 692ms
225:	learn: 0.1869731	total: 568ms	remaining: 689ms
226:	learn: 0.1862054	total: 571ms	remaining: 686ms
227:	learn: 0.1855848	total: 573ms	remaining: 683ms
228:	learn: 0.1850941	total: 575ms	remaining: 681ms
229:	learn: 0.1844525	total: 577ms	remaining: 678ms
230:	learn: 0.1836773	total: 580ms	remaining: 675ms
231:	learn: 0.1825203	total: 582ms	remaining: 672ms
232:	learn: 0.1815767	total: 584ms	remaining: 670ms
233:	learn: 0.1811055	total: 586ms	remaining: 667ms
234:	learn: 0.1803454	total: 589ms	remaining: 664ms
235:	learn: 0.1794879	total: 591ms	remaining: 661ms
236:	learn: 0.1787978	total: 593ms	remaining: 658ms
237:	learn: 0.1779501	total: 595ms	remaining: 655ms
238:	learn: 0.1774108	total: 597ms	remaining: 652ms
239:	learn: 0.1766981	total: 600ms	remaining: 649ms
240:	learn: 0.1760557	total: 602ms	remaining: 647ms
241:	learn: 0.1753942	total: 604ms	remaining: 644ms
242:	learn: 0.1747425	total: 606ms	remaining: 641ms
243:	learn: 0.1739964	total: 608ms	remaining: 638ms
244:	learn: 0.1731781	total: 610ms	remaining: 635ms
245:	learn: 0.1724132	total: 612ms	remaining: 632ms
246:	learn: 0.1716620	total: 615ms	remaining: 630ms
247:	learn: 0.1707502	total: 617ms	remaining: 627ms
248:	learn: 0.1700832	total: 619ms	remaining: 624ms
249:	learn: 0.1692462	total: 621ms	remaining: 621ms
250:	learn: 0.1686694	total: 623ms	remaining: 618ms
251:	learn: 0.1681687	total: 626ms	remaining: 616ms
252:	learn: 0.1675249	total: 628ms	remaining: 613ms
253:	learn: 0.1666296	total: 630ms	remaining: 610ms
254:	learn: 0.1659256	total: 632ms	remaining: 607ms
255:	learn: 0.1653105	total: 634ms	remaining: 605ms
256:	learn: 0.1648230	total: 637ms	remaining: 602ms
257:	learn: 0.1641936	total: 639ms	remaining: 599ms
258:	learn: 0.1633843	total: 641ms	remaining: 597ms
259:	learn: 0.1629782	total: 643ms	remaining: 594ms
260:	learn: 0.1624199	total: 646ms	remaining: 591ms
261:	learn: 0.1617980	total: 648ms	remaining: 589ms
262:	learn: 0.1611492	total: 650ms	remaining: 586ms
263:	learn: 0.1605248	total: 652ms	remaining: 583ms
264:	learn: 0.1597848	total: 655ms	remaining: 580ms
265:	learn: 0.1592586	total: 657ms	remaining: 578ms
266:	learn: 0.1586311	total: 659ms	remaining: 575ms
267:	learn: 0.1580072	total: 661ms	remaining: 572ms
268:	learn: 0.1574220	total: 663ms	remaining: 570ms
269:	learn: 0.1568800	total: 665ms	remaining: 567ms
270:	learn: 0.1561163	total: 668ms	remaining: 564ms
271:	learn: 0.1556330	total: 670ms	remaining: 561ms
272:	learn: 0.1551553	total: 672ms	remaining: 559ms
273:	learn: 0.1546383	total: 674ms	remaining: 556ms
274:	learn: 0.1539709	total: 677ms	remaining: 554ms
275:	learn: 0.1534251	total: 679ms	remaining: 551ms
276:	learn: 0.1529777	total: 681ms	remaining: 548ms
277:	learn: 0.1525679	total: 683ms	remaining: 545ms
278:	learn: 0.1519323	total: 685ms	remaining: 543ms
279:	learn: 0.1514862	total: 687ms	remaining: 540ms
280:	learn: 0.1508028	total: 690ms	remaining: 537ms
281:	learn: 0.1500377	total: 692ms	remaining: 535ms
282:	learn: 0.1494367	total: 694ms	remaining: 532ms
283:	learn: 0.1489622	total: 696ms	remaining: 530ms
284:	learn: 0.1482719	total: 699ms	remaining: 527ms
285:	learn: 0.1476924	total: 702ms	remaining: 525ms
286:	learn: 0.1472179	total: 704ms	remaining: 523ms
287:	learn: 0.1465499	total: 706ms	remaining: 520ms
288:	learn: 0.1460811	total: 709ms	remaining: 517ms
289:	learn: 0.1453631	total: 711ms	remaining: 515ms
290:	learn: 0.1448399	total: 713ms	remaining: 512ms
291:	learn: 0.1443953	total: 715ms	remaining: 510ms
292:	learn: 0.1439762	total: 718ms	remaining: 507ms
293:	learn: 0.1434655	total: 720ms	remaining: 504ms
294:	learn: 0.1428956	total: 722ms	remaining: 502ms
295:	learn: 0.1423959	total: 724ms	remaining: 499ms
296:	learn: 0.1418938	total: 726ms	remaining: 496ms
297:	learn: 0.1413009	total: 728ms	remaining: 494ms
298:	learn: 0.1409128	total: 730ms	remaining: 491ms
299:	learn: 0.1405103	total: 733ms	remaining: 488ms
300:	learn: 0.1400817	total: 735ms	remaining: 486ms
301:	learn: 0.1395934	total: 737ms	remaining: 483ms
302:	learn: 0.1391561	total: 739ms	remaining: 481ms
303:	learn: 0.1386506	total: 743ms	remaining: 479ms
304:	learn: 0.1381796	total: 745ms	remaining: 476ms
305:	learn: 0.1376347	total: 747ms	remaining: 474ms
306:	learn: 0.1370414	total: 749ms	remaining: 471ms
307:	learn: 0.1363608	total: 751ms	remaining: 468ms
308:	learn: 0.1358994	total: 754ms	remaining: 466ms
309:	learn: 0.1355366	total: 756ms	remaining: 463ms
310:	learn: 0.1349880	total: 758ms	remaining: 461ms
311:	learn: 0.1343588	total: 760ms	remaining: 458ms
312:	learn: 0.1338518	total: 763ms	remaining: 456ms
313:	learn: 0.1335693	total: 765ms	remaining: 453ms
314:	learn: 0.1330361	total: 767ms	remaining: 451ms
315:	learn: 0.1324319	total: 769ms	remaining: 448ms
316:	learn: 0.1319425	total: 772ms	remaining: 446ms
317:	learn: 0.1314257	total: 774ms	remaining: 443ms
318:	learn: 0.1308850	total: 776ms	remaining: 440ms
319:	learn: 0.1304707	total: 778ms	remaining: 438ms
320:	learn: 0.1300538	total: 781ms	remaining: 435ms
321:	learn: 0.1295836	total: 783ms	remaining: 433ms
322:	learn: 0.1290208	total: 785ms	remaining: 430ms
323:	learn: 0.1284159	total: 787ms	remaining: 428ms
324:	learn: 0.1280935	total: 789ms	remaining: 425ms
325:	learn: 0.1277006	total: 792ms	remaining: 422ms
326:	learn: 0.1272526	total: 794ms	remaining: 420ms
327:	learn: 0.1268310	total: 796ms	remaining: 417ms
328:	learn: 0.1262959	total: 798ms	remaining: 415ms
329:	learn: 0.1257833	total: 800ms	remaining: 412ms
330:	learn: 0.1253039	total: 802ms	remaining: 410ms
331:	learn: 0.1248702	total: 805ms	remaining: 407ms
332:	learn: 0.1243610	total: 807ms	remaining: 405ms
333:	learn: 0.1240368	total: 809ms	remaining: 402ms
334:	learn: 0.1235977	total: 811ms	remaining: 400ms
335:	learn: 0.1231060	total: 813ms	remaining: 397ms
336:	learn: 0.1224917	total: 816ms	remaining: 394ms
337:	learn: 0.1220091	total: 818ms	remaining: 392ms
338:	learn: 0.1215084	total: 820ms	remaining: 389ms
339:	learn: 0.1210256	total: 822ms	remaining: 387ms
340:	learn: 0.1206214	total: 824ms	remaining: 384ms
341:	learn: 0.1201556	total: 827ms	remaining: 382ms
342:	learn: 0.1196272	total: 829ms	remaining: 379ms
343:	learn: 0.1192256	total: 831ms	remaining: 377ms
344:	learn: 0.1188489	total: 833ms	remaining: 374ms
345:	learn: 0.1184727	total: 835ms	remaining: 372ms
346:	learn: 0.1181508	total: 838ms	remaining: 370ms
347:	learn: 0.1176209	total: 840ms	remaining: 367ms
348:	learn: 0.1169977	total: 842ms	remaining: 365ms
349:	learn: 0.1163403	total: 845ms	remaining: 362ms
350:	learn: 0.1160772	total: 847ms	remaining: 359ms
351:	learn: 0.1158275	total: 849ms	remaining: 357ms
352:	learn: 0.1153470	total: 851ms	remaining: 354ms
353:	learn: 0.1150128	total: 853ms	remaining: 352ms
354:	learn: 0.1144420	total: 856ms	remaining: 349ms
355:	learn: 0.1139903	total: 858ms	remaining: 347ms
356:	learn: 0.1135733	total: 860ms	remaining: 344ms
357:	learn: 0.1131726	total: 862ms	remaining: 342ms
358:	learn: 0.1127556	total: 864ms	remaining: 339ms
359:	learn: 0.1123844	total: 866ms	remaining: 337ms
360:	learn: 0.1120002	total: 869ms	remaining: 334ms
361:	learn: 0.1116088	total: 871ms	remaining: 332ms
362:	learn: 0.1111553	total: 873ms	remaining: 329ms
363:	learn: 0.1108368	total: 875ms	remaining: 327ms
364:	learn: 0.1105773	total: 877ms	remaining: 324ms
365:	learn: 0.1102860	total: 879ms	remaining: 322ms
366:	learn: 0.1098429	total: 882ms	remaining: 319ms
367:	learn: 0.1093239	total: 884ms	remaining: 317ms
368:	learn: 0.1087945	total: 886ms	remaining: 315ms
369:	learn: 0.1082443	total: 888ms	remaining: 312ms
370:	learn: 0.1078512	total: 890ms	remaining: 310ms
371:	learn: 0.1075023	total: 892ms	remaining: 307ms
372:	learn: 0.1070643	total: 895ms	remaining: 305ms
373:	learn: 0.1066039	total: 897ms	remaining: 302ms
374:	learn: 0.1061825	total: 900ms	remaining: 300ms
375:	learn: 0.1058556	total: 902ms	remaining: 298ms
376:	learn: 0.1054415	total: 904ms	remaining: 295ms
377:	learn: 0.1050419	total: 907ms	remaining: 293ms
378:	learn: 0.1046679	total: 909ms	remaining: 290ms
379:	learn: 0.1041818	total: 911ms	remaining: 288ms
380:	learn: 0.1038469	total: 913ms	remaining: 285ms
381:	learn: 0.1035670	total: 916ms	remaining: 283ms
382:	learn: 0.1030641	total: 918ms	remaining: 280ms
383:	learn: 0.1026412	total: 920ms	remaining: 278ms
384:	learn: 0.1022798	total: 922ms	remaining: 275ms
385:	learn: 0.1019102	total: 924ms	remaining: 273ms
386:	learn: 0.1015065	total: 926ms	remaining: 270ms
387:	learn: 0.1010912	total: 929ms	remaining: 268ms
388:	learn: 0.1007510	total: 931ms	remaining: 266ms
389:	learn: 0.1003051	total: 933ms	remaining: 263ms
390:	learn: 0.0999910	total: 936ms	remaining: 261ms
391:	learn: 0.0997395	total: 938ms	remaining: 258ms
392:	learn: 0.0994833	total: 940ms	remaining: 256ms
393:	learn: 0.0992163	total: 942ms	remaining: 254ms
394:	learn: 0.0988594	total: 945ms	remaining: 251ms
395:	learn: 0.0985299	total: 947ms	remaining: 249ms
396:	learn: 0.0981598	total: 949ms	remaining: 246ms
397:	learn: 0.0979067	total: 951ms	remaining: 244ms
398:	learn: 0.0976240	total: 953ms	remaining: 241ms
399:	learn: 0.0972187	total: 956ms	remaining: 239ms
400:	learn: 0.0969455	total: 958ms	remaining: 237ms
401:	learn: 0.0966383	total: 960ms	remaining: 234ms
402:	learn: 0.0962970	total: 963ms	remaining: 232ms
403:	learn: 0.0960680	total: 965ms	remaining: 229ms
404:	learn: 0.0957156	total: 967ms	remaining: 227ms
405:	learn: 0.0954584	total: 969ms	remaining: 224ms
406:	learn: 0.0950046	total: 972ms	remaining: 222ms
407:	learn: 0.0946970	total: 974ms	remaining: 220ms
408:	learn: 0.0944119	total: 976ms	remaining: 217ms
409:	learn: 0.0940386	total: 978ms	remaining: 215ms
410:	learn: 0.0937701	total: 980ms	remaining: 212ms
411:	learn: 0.0933546	total: 982ms	remaining: 210ms
412:	learn: 0.0930306	total: 985ms	remaining: 207ms
413:	learn: 0.0926945	total: 987ms	remaining: 205ms
414:	learn: 0.0924366	total: 989ms	remaining: 203ms
415:	learn: 0.0922373	total: 991ms	remaining: 200ms
416:	learn: 0.0918634	total: 993ms	remaining: 198ms
417:	learn: 0.0915619	total: 996ms	remaining: 195ms
418:	learn: 0.0912721	total: 998ms	remaining: 193ms
419:	learn: 0.0910318	total: 1000ms	remaining: 190ms
420:	learn: 0.0909435	total: 1s	remaining: 188ms
421:	learn: 0.0905679	total: 1s	remaining: 186ms
422:	learn: 0.0902971	total: 1.01s	remaining: 183ms
423:	learn: 0.0899584	total: 1.01s	remaining: 181ms
424:	learn: 0.0896896	total: 1.01s	remaining: 178ms
425:	learn: 0.0893446	total: 1.01s	remaining: 176ms
426:	learn: 0.0890044	total: 1.01s	remaining: 174ms
427:	learn: 0.0886993	total: 1.02s	remaining: 171ms
428:	learn: 0.0883571	total: 1.02s	remaining: 169ms
429:	learn: 0.0880845	total: 1.02s	remaining: 166ms
430:	learn: 0.0878076	total: 1.02s	remaining: 164ms
431:	learn: 0.0874679	total: 1.03s	remaining: 162ms
432:	learn: 0.0871649	total: 1.03s	remaining: 159ms
433:	learn: 0.0867907	total: 1.03s	remaining: 157ms
434:	learn: 0.0864520	total: 1.03s	remaining: 154ms
435:	learn: 0.0861631	total: 1.03s	remaining: 152ms
436:	learn: 0.0858600	total: 1.04s	remaining: 150ms
437:	learn: 0.0855985	total: 1.04s	remaining: 147ms
438:	learn: 0.0853351	total: 1.04s	remaining: 145ms
439:	learn: 0.0849758	total: 1.04s	remaining: 142ms
440:	learn: 0.0846555	total: 1.05s	remaining: 140ms
441:	learn: 0.0844058	total: 1.05s	remaining: 138ms
442:	learn: 0.0841692	total: 1.05s	remaining: 135ms
443:	learn: 0.0839078	total: 1.05s	remaining: 133ms
444:	learn: 0.0836821	total: 1.05s	remaining: 130ms
445:	learn: 0.0833343	total: 1.06s	remaining: 128ms
446:	learn: 0.0830651	total: 1.06s	remaining: 126ms
447:	learn: 0.0826756	total: 1.06s	remaining: 123ms
448:	learn: 0.0823620	total: 1.06s	remaining: 121ms
449:	learn: 0.0820225	total: 1.07s	remaining: 118ms
450:	learn: 0.0817780	total: 1.07s	remaining: 116ms
451:	learn: 0.0814998	total: 1.07s	remaining: 114ms
452:	learn: 0.0812497	total: 1.07s	remaining: 111ms
453:	learn: 0.0810328	total: 1.07s	remaining: 109ms
454:	learn: 0.0806916	total: 1.08s	remaining: 107ms
455:	learn: 0.0804801	total: 1.08s	remaining: 104ms
456:	learn: 0.0802815	total: 1.08s	remaining: 102ms
457:	learn: 0.0800198	total: 1.08s	remaining: 99.4ms
458:	learn: 0.0796654	total: 1.08s	remaining: 97ms
459:	learn: 0.0794070	total: 1.09s	remaining: 94.6ms
460:	learn: 0.0791677	total: 1.09s	remaining: 92.2ms
461:	learn: 0.0789369	total: 1.09s	remaining: 89.8ms
462:	learn: 0.0786962	total: 1.09s	remaining: 87.4ms
463:	learn: 0.0783789	total: 1.1s	remaining: 85.1ms
464:	learn: 0.0781558	total: 1.1s	remaining: 82.7ms
465:	learn: 0.0778957	total: 1.1s	remaining: 80.3ms
466:	learn: 0.0776439	total: 1.1s	remaining: 77.9ms
467:	learn: 0.0773500	total: 1.1s	remaining: 75.6ms
468:	learn: 0.0771112	total: 1.11s	remaining: 73.2ms
469:	learn: 0.0767681	total: 1.11s	remaining: 70.8ms
470:	learn: 0.0765604	total: 1.11s	remaining: 68.4ms
471:	learn: 0.0763216	total: 1.11s	remaining: 66.1ms
472:	learn: 0.0759876	total: 1.11s	remaining: 63.7ms
473:	learn: 0.0757237	total: 1.12s	remaining: 61.3ms
474:	learn: 0.0754865	total: 1.12s	remaining: 58.9ms
475:	learn: 0.0752773	total: 1.12s	remaining: 56.6ms
476:	learn: 0.0749743	total: 1.12s	remaining: 54.2ms
477:	learn: 0.0747243	total: 1.13s	remaining: 51.9ms
478:	learn: 0.0745116	total: 1.13s	remaining: 49.5ms
479:	learn: 0.0742145	total: 1.13s	remaining: 47.2ms
480:	learn: 0.0740424	total: 1.13s	remaining: 44.8ms
481:	learn: 0.0738484	total: 1.14s	remaining: 42.4ms
482:	learn: 0.0735994	total: 1.14s	remaining: 40.1ms
483:	learn: 0.0733726	total: 1.14s	remaining: 37.7ms
484:	learn: 0.0730807	total: 1.14s	remaining: 35.4ms
485:	learn: 0.0728622	total: 1.15s	remaining: 33ms
486:	learn: 0.0725782	total: 1.15s	remaining: 30.6ms
487:	learn: 0.0723929	total: 1.15s	remaining: 28.3ms
488:	learn: 0.0721158	total: 1.15s	remaining: 25.9ms
489:	learn: 0.0718815	total: 1.15s	remaining: 23.6ms
490:	learn: 0.0716106	total: 1.16s	remaining: 21.2ms
491:	learn: 0.0713487	total: 1.16s	remaining: 18.8ms
492:	learn: 0.0711271	total: 1.16s	remaining: 16.5ms
493:	learn: 0.0708740	total: 1.16s	remaining: 14.1ms
494:	learn: 0.0706801	total: 1.17s	remaining: 11.8ms
495:	learn: 0.0704711	total: 1.17s	remaining: 9.42ms
496:	learn: 0.0702406	total: 1.17s	remaining: 7.06ms
497:	learn: 0.0700425	total: 1.17s	remaining: 4.71ms
498:	learn: 0.0698289	total: 1.17s	remaining: 2.35ms
499:	learn: 0.0695832	total: 1.18s	remaining: 0us
0:	learn: 0.6881434	total: 3.29ms	remaining: 1.64s
1:	learn: 0.6817993	total: 5.7ms	remaining: 1.42s
2:	learn: 0.6767294	total: 7.96ms	remaining: 1.32s
3:	learn: 0.6723966	total: 10.4ms	remaining: 1.28s
4:	learn: 0.6674492	total: 12.7ms	remaining: 1.26s
5:	learn: 0.6618513	total: 15ms	remaining: 1.24s
6:	learn: 0.6560563	total: 17.3ms	remaining: 1.22s
7:	learn: 0.6519384	total: 19.5ms	remaining: 1.2s
8:	learn: 0.6455464	total: 21.7ms	remaining: 1.18s
9:	learn: 0.6409382	total: 23.9ms	remaining: 1.17s
10:	learn: 0.6358620	total: 26.1ms	remaining: 1.16s
11:	learn: 0.6309455	total: 28.3ms	remaining: 1.15s
12:	learn: 0.6275687	total: 30.5ms	remaining: 1.14s
13:	learn: 0.6232312	total: 32.7ms	remaining: 1.14s
14:	learn: 0.6177886	total: 34.9ms	remaining: 1.13s
15:	learn: 0.6135005	total: 37.1ms	remaining: 1.12s
16:	learn: 0.6090400	total: 39.3ms	remaining: 1.12s
17:	learn: 0.6042944	total: 41.4ms	remaining: 1.11s
18:	learn: 0.5992685	total: 43.7ms	remaining: 1.1s
19:	learn: 0.5959719	total: 45.8ms	remaining: 1.1s
20:	learn: 0.5913790	total: 48.1ms	remaining: 1.1s
21:	learn: 0.5871699	total: 50.4ms	remaining: 1.09s
22:	learn: 0.5825039	total: 52.7ms	remaining: 1.09s
23:	learn: 0.5773552	total: 54.8ms	remaining: 1.09s
24:	learn: 0.5738272	total: 57ms	remaining: 1.08s
25:	learn: 0.5699874	total: 59.2ms	remaining: 1.08s
26:	learn: 0.5658393	total: 61.4ms	remaining: 1.07s
27:	learn: 0.5616912	total: 63.6ms	remaining: 1.07s
28:	learn: 0.5577648	total: 65.7ms	remaining: 1.07s
29:	learn: 0.5517594	total: 67.9ms	remaining: 1.06s
30:	learn: 0.5476714	total: 70.2ms	remaining: 1.06s
31:	learn: 0.5444366	total: 72.4ms	remaining: 1.06s
32:	learn: 0.5408211	total: 74.6ms	remaining: 1.06s
33:	learn: 0.5367748	total: 76.8ms	remaining: 1.05s
34:	learn: 0.5329893	total: 79ms	remaining: 1.05s
35:	learn: 0.5293705	total: 81.2ms	remaining: 1.05s
36:	learn: 0.5263182	total: 83.4ms	remaining: 1.04s
37:	learn: 0.5228810	total: 85.6ms	remaining: 1.04s
38:	learn: 0.5184576	total: 88.3ms	remaining: 1.04s
39:	learn: 0.5142539	total: 91.5ms	remaining: 1.05s
40:	learn: 0.5112570	total: 94ms	remaining: 1.05s
41:	learn: 0.5085426	total: 96.3ms	remaining: 1.05s
42:	learn: 0.5052005	total: 98.7ms	remaining: 1.05s
43:	learn: 0.5013568	total: 101ms	remaining: 1.05s
44:	learn: 0.4982528	total: 103ms	remaining: 1.04s
45:	learn: 0.4948431	total: 106ms	remaining: 1.04s
46:	learn: 0.4908410	total: 108ms	remaining: 1.04s
47:	learn: 0.4867150	total: 110ms	remaining: 1.03s
48:	learn: 0.4822630	total: 112ms	remaining: 1.03s
49:	learn: 0.4790044	total: 114ms	remaining: 1.03s
50:	learn: 0.4761844	total: 117ms	remaining: 1.02s
51:	learn: 0.4733158	total: 119ms	remaining: 1.02s
52:	learn: 0.4690532	total: 121ms	remaining: 1.02s
53:	learn: 0.4656562	total: 124ms	remaining: 1.02s
54:	learn: 0.4622171	total: 126ms	remaining: 1.02s
55:	learn: 0.4596840	total: 128ms	remaining: 1.01s
56:	learn: 0.4564010	total: 130ms	remaining: 1.01s
57:	learn: 0.4538491	total: 132ms	remaining: 1.01s
58:	learn: 0.4505821	total: 135ms	remaining: 1.01s
59:	learn: 0.4475697	total: 137ms	remaining: 1s
60:	learn: 0.4446738	total: 139ms	remaining: 1s
61:	learn: 0.4418134	total: 141ms	remaining: 998ms
62:	learn: 0.4390802	total: 144ms	remaining: 996ms
63:	learn: 0.4357962	total: 146ms	remaining: 993ms
64:	learn: 0.4326899	total: 148ms	remaining: 992ms
65:	learn: 0.4294651	total: 150ms	remaining: 989ms
66:	learn: 0.4262366	total: 153ms	remaining: 986ms
67:	learn: 0.4229674	total: 155ms	remaining: 983ms
68:	learn: 0.4203466	total: 157ms	remaining: 980ms
69:	learn: 0.4179594	total: 159ms	remaining: 978ms
70:	learn: 0.4155836	total: 161ms	remaining: 975ms
71:	learn: 0.4129918	total: 163ms	remaining: 972ms
72:	learn: 0.4098609	total: 166ms	remaining: 970ms
73:	learn: 0.4077835	total: 168ms	remaining: 967ms
74:	learn: 0.4048433	total: 170ms	remaining: 965ms
75:	learn: 0.4017889	total: 172ms	remaining: 962ms
76:	learn: 0.3988513	total: 175ms	remaining: 959ms
77:	learn: 0.3961960	total: 177ms	remaining: 956ms
78:	learn: 0.3939204	total: 179ms	remaining: 954ms
79:	learn: 0.3914823	total: 181ms	remaining: 952ms
80:	learn: 0.3894617	total: 183ms	remaining: 949ms
81:	learn: 0.3866496	total: 186ms	remaining: 948ms
82:	learn: 0.3838182	total: 188ms	remaining: 945ms
83:	learn: 0.3813485	total: 190ms	remaining: 943ms
84:	learn: 0.3787974	total: 193ms	remaining: 940ms
85:	learn: 0.3761829	total: 195ms	remaining: 938ms
86:	learn: 0.3743821	total: 197ms	remaining: 935ms
87:	learn: 0.3722159	total: 199ms	remaining: 933ms
88:	learn: 0.3692497	total: 201ms	remaining: 930ms
89:	learn: 0.3675127	total: 204ms	remaining: 930ms
90:	learn: 0.3656794	total: 206ms	remaining: 927ms
91:	learn: 0.3629475	total: 208ms	remaining: 924ms
92:	learn: 0.3610316	total: 210ms	remaining: 921ms
93:	learn: 0.3591454	total: 213ms	remaining: 918ms
94:	learn: 0.3569773	total: 215ms	remaining: 916ms
95:	learn: 0.3549315	total: 217ms	remaining: 913ms
96:	learn: 0.3527238	total: 219ms	remaining: 911ms
97:	learn: 0.3503792	total: 222ms	remaining: 909ms
98:	learn: 0.3482760	total: 224ms	remaining: 907ms
99:	learn: 0.3461371	total: 226ms	remaining: 904ms
100:	learn: 0.3439271	total: 228ms	remaining: 902ms
101:	learn: 0.3423085	total: 230ms	remaining: 899ms
102:	learn: 0.3407439	total: 233ms	remaining: 897ms
103:	learn: 0.3387725	total: 235ms	remaining: 894ms
104:	learn: 0.3373234	total: 237ms	remaining: 892ms
105:	learn: 0.3361636	total: 239ms	remaining: 889ms
106:	learn: 0.3345657	total: 242ms	remaining: 887ms
107:	learn: 0.3326594	total: 244ms	remaining: 885ms
108:	learn: 0.3312765	total: 246ms	remaining: 882ms
109:	learn: 0.3296588	total: 248ms	remaining: 880ms
110:	learn: 0.3276657	total: 250ms	remaining: 877ms
111:	learn: 0.3255261	total: 252ms	remaining: 875ms
112:	learn: 0.3241257	total: 255ms	remaining: 872ms
113:	learn: 0.3219659	total: 257ms	remaining: 869ms
114:	learn: 0.3204995	total: 259ms	remaining: 867ms
115:	learn: 0.3188511	total: 261ms	remaining: 865ms
116:	learn: 0.3169123	total: 264ms	remaining: 863ms
117:	learn: 0.3151165	total: 266ms	remaining: 860ms
118:	learn: 0.3136179	total: 268ms	remaining: 858ms
119:	learn: 0.3115659	total: 270ms	remaining: 855ms
120:	learn: 0.3101341	total: 272ms	remaining: 853ms
121:	learn: 0.3084408	total: 274ms	remaining: 850ms
122:	learn: 0.3066964	total: 277ms	remaining: 848ms
123:	learn: 0.3051444	total: 279ms	remaining: 846ms
124:	learn: 0.3029795	total: 282ms	remaining: 846ms
125:	learn: 0.3011427	total: 284ms	remaining: 844ms
126:	learn: 0.2995230	total: 287ms	remaining: 842ms
127:	learn: 0.2982624	total: 289ms	remaining: 841ms
128:	learn: 0.2968574	total: 291ms	remaining: 838ms
129:	learn: 0.2953322	total: 294ms	remaining: 836ms
130:	learn: 0.2943275	total: 296ms	remaining: 833ms
131:	learn: 0.2928500	total: 298ms	remaining: 831ms
132:	learn: 0.2910843	total: 300ms	remaining: 829ms
133:	learn: 0.2895661	total: 303ms	remaining: 826ms
134:	learn: 0.2880197	total: 305ms	remaining: 824ms
135:	learn: 0.2868357	total: 307ms	remaining: 822ms
136:	learn: 0.2854096	total: 309ms	remaining: 820ms
137:	learn: 0.2842783	total: 312ms	remaining: 817ms
138:	learn: 0.2826361	total: 314ms	remaining: 816ms
139:	learn: 0.2809022	total: 316ms	remaining: 813ms
140:	learn: 0.2795276	total: 319ms	remaining: 811ms
141:	learn: 0.2780024	total: 321ms	remaining: 809ms
142:	learn: 0.2767808	total: 323ms	remaining: 806ms
143:	learn: 0.2751526	total: 325ms	remaining: 804ms
144:	learn: 0.2737954	total: 327ms	remaining: 802ms
145:	learn: 0.2724212	total: 330ms	remaining: 799ms
146:	learn: 0.2711888	total: 332ms	remaining: 797ms
147:	learn: 0.2695974	total: 334ms	remaining: 795ms
148:	learn: 0.2681938	total: 336ms	remaining: 792ms
149:	learn: 0.2670329	total: 339ms	remaining: 790ms
150:	learn: 0.2651953	total: 341ms	remaining: 788ms
151:	learn: 0.2637034	total: 343ms	remaining: 786ms
152:	learn: 0.2624267	total: 345ms	remaining: 783ms
153:	learn: 0.2611851	total: 347ms	remaining: 781ms
154:	learn: 0.2597191	total: 350ms	remaining: 778ms
155:	learn: 0.2587711	total: 352ms	remaining: 776ms
156:	learn: 0.2572878	total: 354ms	remaining: 774ms
157:	learn: 0.2561582	total: 357ms	remaining: 772ms
158:	learn: 0.2551232	total: 359ms	remaining: 770ms
159:	learn: 0.2539468	total: 361ms	remaining: 767ms
160:	learn: 0.2527445	total: 363ms	remaining: 765ms
161:	learn: 0.2515809	total: 365ms	remaining: 762ms
162:	learn: 0.2504376	total: 368ms	remaining: 760ms
163:	learn: 0.2491344	total: 370ms	remaining: 758ms
164:	learn: 0.2482263	total: 372ms	remaining: 755ms
165:	learn: 0.2469918	total: 374ms	remaining: 753ms
166:	learn: 0.2457930	total: 376ms	remaining: 751ms
167:	learn: 0.2442534	total: 379ms	remaining: 749ms
168:	learn: 0.2431207	total: 381ms	remaining: 747ms
169:	learn: 0.2420541	total: 385ms	remaining: 748ms
170:	learn: 0.2408402	total: 388ms	remaining: 746ms
171:	learn: 0.2396251	total: 390ms	remaining: 743ms
172:	learn: 0.2385964	total: 392ms	remaining: 741ms
173:	learn: 0.2376230	total: 394ms	remaining: 739ms
174:	learn: 0.2364386	total: 396ms	remaining: 736ms
175:	learn: 0.2352728	total: 399ms	remaining: 734ms
176:	learn: 0.2342799	total: 401ms	remaining: 732ms
177:	learn: 0.2329630	total: 403ms	remaining: 730ms
178:	learn: 0.2320346	total: 406ms	remaining: 727ms
179:	learn: 0.2307107	total: 408ms	remaining: 725ms
180:	learn: 0.2294138	total: 410ms	remaining: 723ms
181:	learn: 0.2282870	total: 412ms	remaining: 720ms
182:	learn: 0.2274085	total: 414ms	remaining: 718ms
183:	learn: 0.2265237	total: 417ms	remaining: 716ms
184:	learn: 0.2257662	total: 419ms	remaining: 713ms
185:	learn: 0.2247448	total: 421ms	remaining: 711ms
186:	learn: 0.2237358	total: 424ms	remaining: 709ms
187:	learn: 0.2229014	total: 426ms	remaining: 706ms
188:	learn: 0.2218070	total: 428ms	remaining: 704ms
189:	learn: 0.2210791	total: 430ms	remaining: 702ms
190:	learn: 0.2200927	total: 432ms	remaining: 699ms
191:	learn: 0.2193561	total: 435ms	remaining: 697ms
192:	learn: 0.2183617	total: 437ms	remaining: 695ms
193:	learn: 0.2176932	total: 439ms	remaining: 693ms
194:	learn: 0.2166488	total: 441ms	remaining: 690ms
195:	learn: 0.2152839	total: 443ms	remaining: 688ms
196:	learn: 0.2144675	total: 446ms	remaining: 686ms
197:	learn: 0.2136544	total: 448ms	remaining: 683ms
198:	learn: 0.2126616	total: 450ms	remaining: 681ms
199:	learn: 0.2116690	total: 452ms	remaining: 679ms
200:	learn: 0.2100827	total: 455ms	remaining: 676ms
201:	learn: 0.2091327	total: 457ms	remaining: 674ms
202:	learn: 0.2081295	total: 459ms	remaining: 672ms
203:	learn: 0.2071521	total: 461ms	remaining: 669ms
204:	learn: 0.2063020	total: 464ms	remaining: 667ms
205:	learn: 0.2053712	total: 466ms	remaining: 665ms
206:	learn: 0.2043821	total: 468ms	remaining: 662ms
207:	learn: 0.2035448	total: 470ms	remaining: 660ms
208:	learn: 0.2025815	total: 472ms	remaining: 658ms
209:	learn: 0.2018292	total: 475ms	remaining: 655ms
210:	learn: 0.2007701	total: 477ms	remaining: 653ms
211:	learn: 0.2000134	total: 479ms	remaining: 651ms
212:	learn: 0.1992210	total: 483ms	remaining: 650ms
213:	learn: 0.1985059	total: 485ms	remaining: 648ms
214:	learn: 0.1975376	total: 487ms	remaining: 645ms
215:	learn: 0.1965438	total: 490ms	remaining: 644ms
216:	learn: 0.1960829	total: 492ms	remaining: 642ms
217:	learn: 0.1952582	total: 495ms	remaining: 640ms
218:	learn: 0.1941814	total: 497ms	remaining: 638ms
219:	learn: 0.1932956	total: 499ms	remaining: 635ms
220:	learn: 0.1925329	total: 501ms	remaining: 633ms
221:	learn: 0.1916855	total: 503ms	remaining: 630ms
222:	learn: 0.1908527	total: 506ms	remaining: 628ms
223:	learn: 0.1900473	total: 508ms	remaining: 626ms
224:	learn: 0.1890610	total: 511ms	remaining: 624ms
225:	learn: 0.1882584	total: 513ms	remaining: 622ms
226:	learn: 0.1872030	total: 516ms	remaining: 620ms
227:	learn: 0.1865375	total: 518ms	remaining: 618ms
228:	learn: 0.1857263	total: 520ms	remaining: 615ms
229:	learn: 0.1848807	total: 522ms	remaining: 613ms
230:	learn: 0.1841694	total: 524ms	remaining: 611ms
231:	learn: 0.1833636	total: 527ms	remaining: 608ms
232:	learn: 0.1826307	total: 529ms	remaining: 606ms
233:	learn: 0.1815764	total: 531ms	remaining: 604ms
234:	learn: 0.1809464	total: 534ms	remaining: 602ms
235:	learn: 0.1801581	total: 536ms	remaining: 599ms
236:	learn: 0.1796006	total: 538ms	remaining: 597ms
237:	learn: 0.1788476	total: 540ms	remaining: 595ms
238:	learn: 0.1780801	total: 542ms	remaining: 592ms
239:	learn: 0.1773128	total: 545ms	remaining: 590ms
240:	learn: 0.1765920	total: 547ms	remaining: 588ms
241:	learn: 0.1759392	total: 549ms	remaining: 586ms
242:	learn: 0.1752790	total: 552ms	remaining: 583ms
243:	learn: 0.1743592	total: 554ms	remaining: 581ms
244:	learn: 0.1735248	total: 556ms	remaining: 579ms
245:	learn: 0.1728032	total: 558ms	remaining: 576ms
246:	learn: 0.1720019	total: 560ms	remaining: 574ms
247:	learn: 0.1714606	total: 562ms	remaining: 572ms
248:	learn: 0.1708223	total: 565ms	remaining: 569ms
249:	learn: 0.1701609	total: 567ms	remaining: 567ms
250:	learn: 0.1695003	total: 569ms	remaining: 564ms
251:	learn: 0.1689526	total: 571ms	remaining: 562ms
252:	learn: 0.1681626	total: 574ms	remaining: 560ms
253:	learn: 0.1673609	total: 578ms	remaining: 560ms
254:	learn: 0.1666192	total: 581ms	remaining: 558ms
255:	learn: 0.1659870	total: 583ms	remaining: 556ms
256:	learn: 0.1654080	total: 585ms	remaining: 553ms
257:	learn: 0.1647303	total: 587ms	remaining: 551ms
258:	learn: 0.1640666	total: 590ms	remaining: 549ms
259:	learn: 0.1636108	total: 592ms	remaining: 546ms
260:	learn: 0.1629195	total: 594ms	remaining: 544ms
261:	learn: 0.1623401	total: 596ms	remaining: 542ms
262:	learn: 0.1617810	total: 599ms	remaining: 539ms
263:	learn: 0.1610498	total: 601ms	remaining: 537ms
264:	learn: 0.1604747	total: 603ms	remaining: 535ms
265:	learn: 0.1597245	total: 605ms	remaining: 533ms
266:	learn: 0.1592562	total: 608ms	remaining: 530ms
267:	learn: 0.1584887	total: 610ms	remaining: 528ms
268:	learn: 0.1580464	total: 612ms	remaining: 526ms
269:	learn: 0.1573061	total: 614ms	remaining: 523ms
270:	learn: 0.1565935	total: 617ms	remaining: 521ms
271:	learn: 0.1562786	total: 619ms	remaining: 519ms
272:	learn: 0.1557142	total: 621ms	remaining: 516ms
273:	learn: 0.1550433	total: 623ms	remaining: 514ms
274:	learn: 0.1544461	total: 625ms	remaining: 512ms
275:	learn: 0.1538430	total: 628ms	remaining: 509ms
276:	learn: 0.1533801	total: 630ms	remaining: 507ms
277:	learn: 0.1528586	total: 632ms	remaining: 505ms
278:	learn: 0.1523430	total: 635ms	remaining: 503ms
279:	learn: 0.1519543	total: 637ms	remaining: 500ms
280:	learn: 0.1514538	total: 639ms	remaining: 498ms
281:	learn: 0.1507611	total: 641ms	remaining: 496ms
282:	learn: 0.1502486	total: 643ms	remaining: 493ms
283:	learn: 0.1498050	total: 646ms	remaining: 491ms
284:	learn: 0.1492233	total: 648ms	remaining: 489ms
285:	learn: 0.1486096	total: 650ms	remaining: 487ms
286:	learn: 0.1481128	total: 653ms	remaining: 484ms
287:	learn: 0.1474613	total: 655ms	remaining: 482ms
288:	learn: 0.1468302	total: 657ms	remaining: 480ms
289:	learn: 0.1462545	total: 659ms	remaining: 477ms
290:	learn: 0.1458345	total: 661ms	remaining: 475ms
291:	learn: 0.1451877	total: 664ms	remaining: 473ms
292:	learn: 0.1446896	total: 666ms	remaining: 470ms
293:	learn: 0.1440451	total: 668ms	remaining: 468ms
294:	learn: 0.1435912	total: 671ms	remaining: 466ms
295:	learn: 0.1431801	total: 674ms	remaining: 464ms
296:	learn: 0.1424783	total: 676ms	remaining: 462ms
297:	learn: 0.1420163	total: 679ms	remaining: 460ms
298:	learn: 0.1414980	total: 681ms	remaining: 458ms
299:	learn: 0.1407602	total: 683ms	remaining: 455ms
300:	learn: 0.1402129	total: 685ms	remaining: 453ms
301:	learn: 0.1396189	total: 687ms	remaining: 451ms
302:	learn: 0.1391721	total: 690ms	remaining: 448ms
303:	learn: 0.1386740	total: 692ms	remaining: 446ms
304:	learn: 0.1380508	total: 694ms	remaining: 444ms
305:	learn: 0.1374624	total: 696ms	remaining: 441ms
306:	learn: 0.1370368	total: 698ms	remaining: 439ms
307:	learn: 0.1364951	total: 701ms	remaining: 437ms
308:	learn: 0.1359733	total: 703ms	remaining: 434ms
309:	learn: 0.1353602	total: 705ms	remaining: 432ms
310:	learn: 0.1347172	total: 708ms	remaining: 430ms
311:	learn: 0.1341767	total: 710ms	remaining: 428ms
312:	learn: 0.1335269	total: 712ms	remaining: 425ms
313:	learn: 0.1331369	total: 715ms	remaining: 423ms
314:	learn: 0.1327359	total: 717ms	remaining: 421ms
315:	learn: 0.1322319	total: 719ms	remaining: 419ms
316:	learn: 0.1317850	total: 721ms	remaining: 416ms
317:	learn: 0.1314360	total: 723ms	remaining: 414ms
318:	learn: 0.1310139	total: 726ms	remaining: 412ms
319:	learn: 0.1304841	total: 728ms	remaining: 409ms
320:	learn: 0.1300109	total: 730ms	remaining: 407ms
321:	learn: 0.1294715	total: 732ms	remaining: 405ms
322:	learn: 0.1289434	total: 735ms	remaining: 402ms
323:	learn: 0.1283780	total: 737ms	remaining: 400ms
324:	learn: 0.1277558	total: 739ms	remaining: 398ms
325:	learn: 0.1272733	total: 741ms	remaining: 396ms
326:	learn: 0.1267984	total: 743ms	remaining: 393ms
327:	learn: 0.1263013	total: 746ms	remaining: 391ms
328:	learn: 0.1256842	total: 748ms	remaining: 389ms
329:	learn: 0.1253814	total: 750ms	remaining: 386ms
330:	learn: 0.1247650	total: 752ms	remaining: 384ms
331:	learn: 0.1243478	total: 755ms	remaining: 382ms
332:	learn: 0.1240803	total: 757ms	remaining: 380ms
333:	learn: 0.1236467	total: 759ms	remaining: 377ms
334:	learn: 0.1231866	total: 761ms	remaining: 375ms
335:	learn: 0.1227050	total: 763ms	remaining: 373ms
336:	learn: 0.1221952	total: 766ms	remaining: 370ms
337:	learn: 0.1217661	total: 768ms	remaining: 368ms
338:	learn: 0.1213898	total: 772ms	remaining: 367ms
339:	learn: 0.1211076	total: 774ms	remaining: 364ms
340:	learn: 0.1208031	total: 776ms	remaining: 362ms
341:	learn: 0.1203534	total: 778ms	remaining: 360ms
342:	learn: 0.1199548	total: 781ms	remaining: 357ms
343:	learn: 0.1195551	total: 783ms	remaining: 355ms
344:	learn: 0.1191690	total: 785ms	remaining: 353ms
345:	learn: 0.1188194	total: 787ms	remaining: 351ms
346:	learn: 0.1183282	total: 790ms	remaining: 348ms
347:	learn: 0.1178921	total: 792ms	remaining: 346ms
348:	learn: 0.1174574	total: 795ms	remaining: 344ms
349:	learn: 0.1170673	total: 797ms	remaining: 342ms
350:	learn: 0.1168024	total: 799ms	remaining: 339ms
351:	learn: 0.1163841	total: 801ms	remaining: 337ms
352:	learn: 0.1160153	total: 804ms	remaining: 335ms
353:	learn: 0.1155992	total: 806ms	remaining: 332ms
354:	learn: 0.1152047	total: 808ms	remaining: 330ms
355:	learn: 0.1147796	total: 811ms	remaining: 328ms
356:	learn: 0.1143545	total: 813ms	remaining: 326ms
357:	learn: 0.1140230	total: 815ms	remaining: 323ms
358:	learn: 0.1137075	total: 817ms	remaining: 321ms
359:	learn: 0.1133093	total: 820ms	remaining: 319ms
360:	learn: 0.1129880	total: 822ms	remaining: 317ms
361:	learn: 0.1125839	total: 824ms	remaining: 314ms
362:	learn: 0.1122150	total: 827ms	remaining: 312ms
363:	learn: 0.1118963	total: 829ms	remaining: 310ms
364:	learn: 0.1115533	total: 831ms	remaining: 307ms
365:	learn: 0.1113158	total: 833ms	remaining: 305ms
366:	learn: 0.1110499	total: 835ms	remaining: 303ms
367:	learn: 0.1106992	total: 838ms	remaining: 300ms
368:	learn: 0.1102689	total: 840ms	remaining: 298ms
369:	learn: 0.1099412	total: 842ms	remaining: 296ms
370:	learn: 0.1095182	total: 844ms	remaining: 294ms
371:	learn: 0.1091148	total: 847ms	remaining: 291ms
372:	learn: 0.1087449	total: 849ms	remaining: 289ms
373:	learn: 0.1083059	total: 851ms	remaining: 287ms
374:	learn: 0.1080003	total: 853ms	remaining: 284ms
375:	learn: 0.1074755	total: 856ms	remaining: 282ms
376:	learn: 0.1071789	total: 858ms	remaining: 280ms
377:	learn: 0.1069598	total: 860ms	remaining: 278ms
378:	learn: 0.1066790	total: 862ms	remaining: 275ms
379:	learn: 0.1063499	total: 864ms	remaining: 273ms
380:	learn: 0.1060874	total: 868ms	remaining: 271ms
381:	learn: 0.1056754	total: 870ms	remaining: 269ms
382:	learn: 0.1052812	total: 872ms	remaining: 267ms
383:	learn: 0.1048637	total: 875ms	remaining: 264ms
384:	learn: 0.1044213	total: 877ms	remaining: 262ms
385:	learn: 0.1039479	total: 879ms	remaining: 260ms
386:	learn: 0.1036550	total: 881ms	remaining: 257ms
387:	learn: 0.1031496	total: 884ms	remaining: 255ms
388:	learn: 0.1028206	total: 886ms	remaining: 253ms
389:	learn: 0.1022936	total: 888ms	remaining: 250ms
390:	learn: 0.1019505	total: 890ms	remaining: 248ms
391:	learn: 0.1016643	total: 892ms	remaining: 246ms
392:	learn: 0.1012562	total: 894ms	remaining: 243ms
393:	learn: 0.1009545	total: 896ms	remaining: 241ms
394:	learn: 0.1006172	total: 899ms	remaining: 239ms
395:	learn: 0.1000850	total: 901ms	remaining: 237ms
396:	learn: 0.0998100	total: 904ms	remaining: 234ms
397:	learn: 0.0995446	total: 906ms	remaining: 232ms
398:	learn: 0.0991797	total: 908ms	remaining: 230ms
399:	learn: 0.0988492	total: 910ms	remaining: 228ms
400:	learn: 0.0985475	total: 912ms	remaining: 225ms
401:	learn: 0.0982952	total: 915ms	remaining: 223ms
402:	learn: 0.0978413	total: 917ms	remaining: 221ms
403:	learn: 0.0975168	total: 919ms	remaining: 218ms
404:	learn: 0.0972452	total: 921ms	remaining: 216ms
405:	learn: 0.0968961	total: 923ms	remaining: 214ms
406:	learn: 0.0966173	total: 926ms	remaining: 211ms
407:	learn: 0.0963147	total: 928ms	remaining: 209ms
408:	learn: 0.0960077	total: 930ms	remaining: 207ms
409:	learn: 0.0955725	total: 932ms	remaining: 205ms
410:	learn: 0.0952132	total: 934ms	remaining: 202ms
411:	learn: 0.0948745	total: 936ms	remaining: 200ms
412:	learn: 0.0945621	total: 939ms	remaining: 198ms
413:	learn: 0.0942022	total: 941ms	remaining: 196ms
414:	learn: 0.0939619	total: 943ms	remaining: 193ms
415:	learn: 0.0936825	total: 946ms	remaining: 191ms
416:	learn: 0.0933646	total: 948ms	remaining: 189ms
417:	learn: 0.0929624	total: 950ms	remaining: 186ms
418:	learn: 0.0927021	total: 952ms	remaining: 184ms
419:	learn: 0.0924550	total: 954ms	remaining: 182ms
420:	learn: 0.0922854	total: 956ms	remaining: 179ms
421:	learn: 0.0918493	total: 959ms	remaining: 177ms
422:	learn: 0.0915252	total: 961ms	remaining: 175ms
423:	learn: 0.0912322	total: 966ms	remaining: 173ms
424:	learn: 0.0909183	total: 968ms	remaining: 171ms
425:	learn: 0.0906302	total: 970ms	remaining: 169ms
426:	learn: 0.0902706	total: 972ms	remaining: 166ms
427:	learn: 0.0899772	total: 975ms	remaining: 164ms
428:	learn: 0.0897417	total: 977ms	remaining: 162ms
429:	learn: 0.0893379	total: 979ms	remaining: 159ms
430:	learn: 0.0890596	total: 981ms	remaining: 157ms
431:	learn: 0.0888240	total: 983ms	remaining: 155ms
432:	learn: 0.0884649	total: 985ms	remaining: 152ms
433:	learn: 0.0881817	total: 988ms	remaining: 150ms
434:	learn: 0.0879060	total: 990ms	remaining: 148ms
435:	learn: 0.0876996	total: 992ms	remaining: 146ms
436:	learn: 0.0873268	total: 994ms	remaining: 143ms
437:	learn: 0.0870225	total: 996ms	remaining: 141ms
438:	learn: 0.0866623	total: 999ms	remaining: 139ms
439:	learn: 0.0863992	total: 1s	remaining: 137ms
440:	learn: 0.0861607	total: 1s	remaining: 134ms
441:	learn: 0.0858660	total: 1.01s	remaining: 132ms
442:	learn: 0.0856419	total: 1.01s	remaining: 130ms
443:	learn: 0.0853536	total: 1.01s	remaining: 128ms
444:	learn: 0.0851330	total: 1.01s	remaining: 125ms
445:	learn: 0.0847968	total: 1.02s	remaining: 123ms
446:	learn: 0.0845627	total: 1.02s	remaining: 121ms
447:	learn: 0.0843162	total: 1.02s	remaining: 119ms
448:	learn: 0.0839431	total: 1.02s	remaining: 116ms
449:	learn: 0.0837008	total: 1.03s	remaining: 114ms
450:	learn: 0.0833913	total: 1.03s	remaining: 112ms
451:	learn: 0.0830501	total: 1.03s	remaining: 109ms
452:	learn: 0.0828065	total: 1.03s	remaining: 107ms
453:	learn: 0.0825507	total: 1.03s	remaining: 105ms
454:	learn: 0.0822634	total: 1.04s	remaining: 103ms
455:	learn: 0.0820341	total: 1.04s	remaining: 100ms
456:	learn: 0.0818053	total: 1.04s	remaining: 98.1ms
457:	learn: 0.0814909	total: 1.04s	remaining: 95.9ms
458:	learn: 0.0813106	total: 1.05s	remaining: 93.6ms
459:	learn: 0.0810714	total: 1.05s	remaining: 91.3ms
460:	learn: 0.0807831	total: 1.05s	remaining: 89ms
461:	learn: 0.0804449	total: 1.05s	remaining: 86.8ms
462:	learn: 0.0801602	total: 1.06s	remaining: 84.5ms
463:	learn: 0.0798678	total: 1.06s	remaining: 82.4ms
464:	learn: 0.0796306	total: 1.06s	remaining: 80.1ms
465:	learn: 0.0794094	total: 1.07s	remaining: 77.9ms
466:	learn: 0.0791182	total: 1.07s	remaining: 75.6ms
467:	learn: 0.0788313	total: 1.07s	remaining: 73.3ms
468:	learn: 0.0785495	total: 1.07s	remaining: 71ms
469:	learn: 0.0782569	total: 1.08s	remaining: 68.7ms
470:	learn: 0.0779966	total: 1.08s	remaining: 66.4ms
471:	learn: 0.0777247	total: 1.08s	remaining: 64.2ms
472:	learn: 0.0774754	total: 1.08s	remaining: 61.9ms
473:	learn: 0.0772019	total: 1.09s	remaining: 59.6ms
474:	learn: 0.0769579	total: 1.09s	remaining: 57.3ms
475:	learn: 0.0767557	total: 1.09s	remaining: 55ms
476:	learn: 0.0765703	total: 1.09s	remaining: 52.8ms
477:	learn: 0.0763842	total: 1.1s	remaining: 50.5ms
478:	learn: 0.0760999	total: 1.1s	remaining: 48.2ms
479:	learn: 0.0758233	total: 1.1s	remaining: 45.9ms
480:	learn: 0.0756178	total: 1.1s	remaining: 43.6ms
481:	learn: 0.0754245	total: 1.1s	remaining: 41.3ms
482:	learn: 0.0752388	total: 1.11s	remaining: 39ms
483:	learn: 0.0749493	total: 1.11s	remaining: 36.7ms
484:	learn: 0.0747504	total: 1.11s	remaining: 34.4ms
485:	learn: 0.0745433	total: 1.11s	remaining: 32.1ms
486:	learn: 0.0743012	total: 1.12s	remaining: 29.8ms
487:	learn: 0.0740716	total: 1.12s	remaining: 27.5ms
488:	learn: 0.0738021	total: 1.12s	remaining: 25.2ms
489:	learn: 0.0736068	total: 1.12s	remaining: 22.9ms
490:	learn: 0.0733312	total: 1.13s	remaining: 20.6ms
491:	learn: 0.0731382	total: 1.13s	remaining: 18.3ms
492:	learn: 0.0728669	total: 1.13s	remaining: 16.1ms
493:	learn: 0.0725719	total: 1.13s	remaining: 13.8ms
494:	learn: 0.0723780	total: 1.14s	remaining: 11.5ms
495:	learn: 0.0721745	total: 1.14s	remaining: 9.18ms
496:	learn: 0.0720172	total: 1.14s	remaining: 6.88ms
497:	learn: 0.0718063	total: 1.14s	remaining: 4.59ms
498:	learn: 0.0715358	total: 1.14s	remaining: 2.29ms
499:	learn: 0.0712717	total: 1.15s	remaining: 0us
0:	learn: 0.6878430	total: 3.97ms	remaining: 1.98s
1:	learn: 0.6815829	total: 6.33ms	remaining: 1.57s
2:	learn: 0.6752045	total: 8.72ms	remaining: 1.44s
3:	learn: 0.6691032	total: 11ms	remaining: 1.37s
4:	learn: 0.6641107	total: 13.3ms	remaining: 1.31s
5:	learn: 0.6593051	total: 15.5ms	remaining: 1.28s
6:	learn: 0.6537241	total: 17.9ms	remaining: 1.26s
7:	learn: 0.6463333	total: 20.1ms	remaining: 1.24s
8:	learn: 0.6401055	total: 22.2ms	remaining: 1.21s
9:	learn: 0.6360632	total: 24.4ms	remaining: 1.2s
10:	learn: 0.6308937	total: 26.6ms	remaining: 1.18s
11:	learn: 0.6265293	total: 28.9ms	remaining: 1.17s
12:	learn: 0.6224564	total: 31.1ms	remaining: 1.16s
13:	learn: 0.6158117	total: 33.4ms	remaining: 1.16s
14:	learn: 0.6108503	total: 35.9ms	remaining: 1.16s
15:	learn: 0.6059853	total: 38.2ms	remaining: 1.16s
16:	learn: 0.6014550	total: 40.6ms	remaining: 1.15s
17:	learn: 0.5965023	total: 42.9ms	remaining: 1.15s
18:	learn: 0.5920687	total: 45.2ms	remaining: 1.14s
19:	learn: 0.5877537	total: 47.4ms	remaining: 1.14s
20:	learn: 0.5828715	total: 49.6ms	remaining: 1.13s
21:	learn: 0.5781439	total: 51.9ms	remaining: 1.13s
22:	learn: 0.5744309	total: 55ms	remaining: 1.14s
23:	learn: 0.5698276	total: 57.4ms	remaining: 1.14s
24:	learn: 0.5651671	total: 59.7ms	remaining: 1.13s
25:	learn: 0.5607397	total: 61.9ms	remaining: 1.13s
26:	learn: 0.5559441	total: 64.2ms	remaining: 1.12s
27:	learn: 0.5515302	total: 66.5ms	remaining: 1.12s
28:	learn: 0.5453946	total: 68.8ms	remaining: 1.12s
29:	learn: 0.5412454	total: 71ms	remaining: 1.11s
30:	learn: 0.5369850	total: 73.3ms	remaining: 1.11s
31:	learn: 0.5342810	total: 75.7ms	remaining: 1.11s
32:	learn: 0.5292541	total: 78ms	remaining: 1.1s
33:	learn: 0.5263172	total: 80.2ms	remaining: 1.1s
34:	learn: 0.5223302	total: 82.6ms	remaining: 1.1s
35:	learn: 0.5186069	total: 84.9ms	remaining: 1.09s
36:	learn: 0.5146159	total: 87.1ms	remaining: 1.09s
37:	learn: 0.5116652	total: 89.4ms	remaining: 1.09s
38:	learn: 0.5075041	total: 91.8ms	remaining: 1.08s
39:	learn: 0.5047350	total: 94.1ms	remaining: 1.08s
40:	learn: 0.5016186	total: 96.9ms	remaining: 1.08s
41:	learn: 0.4986410	total: 99.3ms	remaining: 1.08s
42:	learn: 0.4958315	total: 102ms	remaining: 1.08s
43:	learn: 0.4918416	total: 104ms	remaining: 1.08s
44:	learn: 0.4887462	total: 106ms	remaining: 1.07s
45:	learn: 0.4862275	total: 108ms	remaining: 1.07s
46:	learn: 0.4824638	total: 111ms	remaining: 1.07s
47:	learn: 0.4788698	total: 113ms	remaining: 1.06s
48:	learn: 0.4751924	total: 115ms	remaining: 1.06s
49:	learn: 0.4724991	total: 117ms	remaining: 1.06s
50:	learn: 0.4694574	total: 120ms	remaining: 1.05s
51:	learn: 0.4660359	total: 122ms	remaining: 1.05s
52:	learn: 0.4636423	total: 124ms	remaining: 1.05s
53:	learn: 0.4604771	total: 126ms	remaining: 1.04s
54:	learn: 0.4582777	total: 129ms	remaining: 1.04s
55:	learn: 0.4553313	total: 131ms	remaining: 1.04s
56:	learn: 0.4525175	total: 133ms	remaining: 1.04s
57:	learn: 0.4500211	total: 136ms	remaining: 1.03s
58:	learn: 0.4478666	total: 138ms	remaining: 1.03s
59:	learn: 0.4441613	total: 141ms	remaining: 1.03s
60:	learn: 0.4412372	total: 143ms	remaining: 1.03s
61:	learn: 0.4373837	total: 146ms	remaining: 1.03s
62:	learn: 0.4346256	total: 148ms	remaining: 1.02s
63:	learn: 0.4320658	total: 150ms	remaining: 1.02s
64:	learn: 0.4290027	total: 153ms	remaining: 1.02s
65:	learn: 0.4262409	total: 157ms	remaining: 1.03s
66:	learn: 0.4234151	total: 159ms	remaining: 1.03s
67:	learn: 0.4208195	total: 161ms	remaining: 1.02s
68:	learn: 0.4173193	total: 164ms	remaining: 1.02s
69:	learn: 0.4150181	total: 166ms	remaining: 1.02s
70:	learn: 0.4124102	total: 168ms	remaining: 1.01s
71:	learn: 0.4095540	total: 171ms	remaining: 1.01s
72:	learn: 0.4074260	total: 173ms	remaining: 1.01s
73:	learn: 0.4046415	total: 175ms	remaining: 1.01s
74:	learn: 0.4017510	total: 177ms	remaining: 1s
75:	learn: 0.3984904	total: 180ms	remaining: 1s
76:	learn: 0.3959053	total: 182ms	remaining: 1000ms
77:	learn: 0.3935912	total: 184ms	remaining: 997ms
78:	learn: 0.3908130	total: 187ms	remaining: 994ms
79:	learn: 0.3890528	total: 189ms	remaining: 992ms
80:	learn: 0.3863627	total: 191ms	remaining: 989ms
81:	learn: 0.3842287	total: 194ms	remaining: 987ms
82:	learn: 0.3822122	total: 196ms	remaining: 984ms
83:	learn: 0.3794104	total: 198ms	remaining: 982ms
84:	learn: 0.3776985	total: 201ms	remaining: 979ms
85:	learn: 0.3751181	total: 203ms	remaining: 976ms
86:	learn: 0.3732622	total: 205ms	remaining: 973ms
87:	learn: 0.3715949	total: 207ms	remaining: 971ms
88:	learn: 0.3697628	total: 210ms	remaining: 968ms
89:	learn: 0.3677751	total: 212ms	remaining: 965ms
90:	learn: 0.3657805	total: 214ms	remaining: 963ms
91:	learn: 0.3628768	total: 216ms	remaining: 960ms
92:	learn: 0.3605602	total: 219ms	remaining: 957ms
93:	learn: 0.3590138	total: 221ms	remaining: 954ms
94:	learn: 0.3572330	total: 223ms	remaining: 951ms
95:	learn: 0.3555873	total: 226ms	remaining: 949ms
96:	learn: 0.3540395	total: 228ms	remaining: 947ms
97:	learn: 0.3519037	total: 230ms	remaining: 945ms
98:	learn: 0.3501547	total: 232ms	remaining: 942ms
99:	learn: 0.3473992	total: 235ms	remaining: 939ms
100:	learn: 0.3454857	total: 237ms	remaining: 936ms
101:	learn: 0.3440746	total: 239ms	remaining: 934ms
102:	learn: 0.3428414	total: 242ms	remaining: 931ms
103:	learn: 0.3409780	total: 244ms	remaining: 929ms
104:	learn: 0.3393085	total: 246ms	remaining: 926ms
105:	learn: 0.3370658	total: 249ms	remaining: 924ms
106:	learn: 0.3350547	total: 252ms	remaining: 925ms
107:	learn: 0.3333121	total: 254ms	remaining: 923ms
108:	learn: 0.3316472	total: 256ms	remaining: 920ms
109:	learn: 0.3299331	total: 259ms	remaining: 917ms
110:	learn: 0.3280070	total: 261ms	remaining: 914ms
111:	learn: 0.3257353	total: 263ms	remaining: 912ms
112:	learn: 0.3242210	total: 265ms	remaining: 909ms
113:	learn: 0.3218111	total: 268ms	remaining: 907ms
114:	learn: 0.3197758	total: 270ms	remaining: 904ms
115:	learn: 0.3182254	total: 272ms	remaining: 902ms
116:	learn: 0.3163147	total: 275ms	remaining: 899ms
117:	learn: 0.3149220	total: 277ms	remaining: 897ms
118:	learn: 0.3136786	total: 279ms	remaining: 894ms
119:	learn: 0.3117700	total: 282ms	remaining: 892ms
120:	learn: 0.3101465	total: 284ms	remaining: 889ms
121:	learn: 0.3083932	total: 286ms	remaining: 887ms
122:	learn: 0.3064015	total: 289ms	remaining: 885ms
123:	learn: 0.3052009	total: 292ms	remaining: 884ms
124:	learn: 0.3028477	total: 295ms	remaining: 884ms
125:	learn: 0.3013044	total: 297ms	remaining: 881ms
126:	learn: 0.2998684	total: 299ms	remaining: 879ms
127:	learn: 0.2983498	total: 301ms	remaining: 876ms
128:	learn: 0.2964663	total: 304ms	remaining: 874ms
129:	learn: 0.2951197	total: 306ms	remaining: 871ms
130:	learn: 0.2935035	total: 308ms	remaining: 868ms
131:	learn: 0.2920221	total: 311ms	remaining: 866ms
132:	learn: 0.2907403	total: 313ms	remaining: 863ms
133:	learn: 0.2889873	total: 315ms	remaining: 860ms
134:	learn: 0.2880048	total: 317ms	remaining: 858ms
135:	learn: 0.2867445	total: 319ms	remaining: 855ms
136:	learn: 0.2851194	total: 322ms	remaining: 852ms
137:	learn: 0.2837270	total: 324ms	remaining: 850ms
138:	learn: 0.2819151	total: 326ms	remaining: 847ms
139:	learn: 0.2806457	total: 328ms	remaining: 844ms
140:	learn: 0.2795289	total: 331ms	remaining: 842ms
141:	learn: 0.2783040	total: 333ms	remaining: 839ms
142:	learn: 0.2770267	total: 335ms	remaining: 837ms
143:	learn: 0.2756222	total: 337ms	remaining: 834ms
144:	learn: 0.2742442	total: 340ms	remaining: 831ms
145:	learn: 0.2730693	total: 342ms	remaining: 829ms
146:	learn: 0.2720061	total: 344ms	remaining: 826ms
147:	learn: 0.2705113	total: 348ms	remaining: 827ms
148:	learn: 0.2693130	total: 350ms	remaining: 825ms
149:	learn: 0.2679981	total: 352ms	remaining: 822ms
150:	learn: 0.2667032	total: 355ms	remaining: 820ms
151:	learn: 0.2653766	total: 357ms	remaining: 817ms
152:	learn: 0.2638372	total: 359ms	remaining: 814ms
153:	learn: 0.2627210	total: 361ms	remaining: 812ms
154:	learn: 0.2612002	total: 364ms	remaining: 809ms
155:	learn: 0.2601681	total: 366ms	remaining: 807ms
156:	learn: 0.2592490	total: 368ms	remaining: 804ms
157:	learn: 0.2580486	total: 370ms	remaining: 802ms
158:	learn: 0.2567666	total: 373ms	remaining: 799ms
159:	learn: 0.2558372	total: 375ms	remaining: 797ms
160:	learn: 0.2548762	total: 377ms	remaining: 794ms
161:	learn: 0.2531939	total: 379ms	remaining: 792ms
162:	learn: 0.2521440	total: 382ms	remaining: 789ms
163:	learn: 0.2506145	total: 384ms	remaining: 787ms
164:	learn: 0.2496069	total: 386ms	remaining: 784ms
165:	learn: 0.2482141	total: 389ms	remaining: 782ms
166:	learn: 0.2470040	total: 391ms	remaining: 780ms
167:	learn: 0.2456256	total: 393ms	remaining: 777ms
168:	learn: 0.2446583	total: 396ms	remaining: 775ms
169:	learn: 0.2438612	total: 398ms	remaining: 772ms
170:	learn: 0.2424424	total: 400ms	remaining: 770ms
171:	learn: 0.2414154	total: 402ms	remaining: 767ms
172:	learn: 0.2406153	total: 405ms	remaining: 765ms
173:	learn: 0.2395990	total: 407ms	remaining: 763ms
174:	learn: 0.2381846	total: 409ms	remaining: 760ms
175:	learn: 0.2374787	total: 412ms	remaining: 758ms
176:	learn: 0.2363830	total: 414ms	remaining: 756ms
177:	learn: 0.2349745	total: 416ms	remaining: 753ms
178:	learn: 0.2340695	total: 419ms	remaining: 751ms
179:	learn: 0.2330175	total: 421ms	remaining: 748ms
180:	learn: 0.2317692	total: 423ms	remaining: 746ms
181:	learn: 0.2306759	total: 426ms	remaining: 744ms
182:	learn: 0.2297521	total: 428ms	remaining: 741ms
183:	learn: 0.2289003	total: 430ms	remaining: 739ms
184:	learn: 0.2281794	total: 432ms	remaining: 736ms
185:	learn: 0.2273863	total: 434ms	remaining: 733ms
186:	learn: 0.2261068	total: 437ms	remaining: 731ms
187:	learn: 0.2249411	total: 439ms	remaining: 729ms
188:	learn: 0.2239022	total: 441ms	remaining: 726ms
189:	learn: 0.2229674	total: 446ms	remaining: 727ms
190:	learn: 0.2221231	total: 448ms	remaining: 724ms
191:	learn: 0.2212878	total: 450ms	remaining: 722ms
192:	learn: 0.2202624	total: 452ms	remaining: 720ms
193:	learn: 0.2194954	total: 455ms	remaining: 717ms
194:	learn: 0.2185497	total: 457ms	remaining: 715ms
195:	learn: 0.2175579	total: 459ms	remaining: 712ms
196:	learn: 0.2166932	total: 461ms	remaining: 710ms
197:	learn: 0.2157318	total: 464ms	remaining: 707ms
198:	learn: 0.2142627	total: 466ms	remaining: 705ms
199:	learn: 0.2132546	total: 468ms	remaining: 702ms
200:	learn: 0.2122960	total: 470ms	remaining: 700ms
201:	learn: 0.2113261	total: 473ms	remaining: 697ms
202:	learn: 0.2101054	total: 475ms	remaining: 695ms
203:	learn: 0.2089941	total: 477ms	remaining: 693ms
204:	learn: 0.2083380	total: 481ms	remaining: 691ms
205:	learn: 0.2071469	total: 483ms	remaining: 689ms
206:	learn: 0.2065200	total: 486ms	remaining: 688ms
207:	learn: 0.2054709	total: 489ms	remaining: 686ms
208:	learn: 0.2045087	total: 491ms	remaining: 683ms
209:	learn: 0.2039344	total: 493ms	remaining: 681ms
210:	learn: 0.2031589	total: 495ms	remaining: 678ms
211:	learn: 0.2024598	total: 498ms	remaining: 676ms
212:	learn: 0.2013593	total: 500ms	remaining: 674ms
213:	learn: 0.2005114	total: 502ms	remaining: 671ms
214:	learn: 0.1997249	total: 504ms	remaining: 669ms
215:	learn: 0.1988753	total: 507ms	remaining: 666ms
216:	learn: 0.1982986	total: 509ms	remaining: 664ms
217:	learn: 0.1970639	total: 511ms	remaining: 661ms
218:	learn: 0.1961587	total: 513ms	remaining: 659ms
219:	learn: 0.1953295	total: 516ms	remaining: 656ms
220:	learn: 0.1945668	total: 518ms	remaining: 654ms
221:	learn: 0.1934386	total: 520ms	remaining: 652ms
222:	learn: 0.1926650	total: 523ms	remaining: 649ms
223:	learn: 0.1920412	total: 525ms	remaining: 647ms
224:	learn: 0.1910205	total: 527ms	remaining: 644ms
225:	learn: 0.1902063	total: 529ms	remaining: 642ms
226:	learn: 0.1890163	total: 532ms	remaining: 639ms
227:	learn: 0.1880042	total: 534ms	remaining: 637ms
228:	learn: 0.1872224	total: 536ms	remaining: 634ms
229:	learn: 0.1862757	total: 539ms	remaining: 632ms
230:	learn: 0.1853608	total: 542ms	remaining: 632ms
231:	learn: 0.1844880	total: 545ms	remaining: 629ms
232:	learn: 0.1837600	total: 547ms	remaining: 627ms
233:	learn: 0.1829314	total: 549ms	remaining: 624ms
234:	learn: 0.1820411	total: 551ms	remaining: 622ms
235:	learn: 0.1814256	total: 554ms	remaining: 619ms
236:	learn: 0.1808000	total: 556ms	remaining: 617ms
237:	learn: 0.1801887	total: 558ms	remaining: 614ms
238:	learn: 0.1796485	total: 560ms	remaining: 612ms
239:	learn: 0.1788578	total: 563ms	remaining: 610ms
240:	learn: 0.1780827	total: 565ms	remaining: 607ms
241:	learn: 0.1775935	total: 567ms	remaining: 605ms
242:	learn: 0.1768632	total: 570ms	remaining: 602ms
243:	learn: 0.1760612	total: 572ms	remaining: 600ms
244:	learn: 0.1751842	total: 574ms	remaining: 598ms
245:	learn: 0.1744771	total: 577ms	remaining: 595ms
246:	learn: 0.1738950	total: 579ms	remaining: 593ms
247:	learn: 0.1731151	total: 581ms	remaining: 590ms
248:	learn: 0.1725961	total: 583ms	remaining: 588ms
249:	learn: 0.1718647	total: 585ms	remaining: 585ms
250:	learn: 0.1712498	total: 588ms	remaining: 583ms
251:	learn: 0.1703768	total: 590ms	remaining: 581ms
252:	learn: 0.1698347	total: 592ms	remaining: 578ms
253:	learn: 0.1691671	total: 595ms	remaining: 576ms
254:	learn: 0.1685164	total: 597ms	remaining: 573ms
255:	learn: 0.1678357	total: 599ms	remaining: 571ms
256:	learn: 0.1671726	total: 601ms	remaining: 569ms
257:	learn: 0.1664381	total: 604ms	remaining: 566ms
258:	learn: 0.1656210	total: 606ms	remaining: 564ms
259:	learn: 0.1650951	total: 608ms	remaining: 561ms
260:	learn: 0.1644720	total: 613ms	remaining: 562ms
261:	learn: 0.1637616	total: 616ms	remaining: 559ms
262:	learn: 0.1630731	total: 618ms	remaining: 557ms
263:	learn: 0.1623744	total: 620ms	remaining: 555ms
264:	learn: 0.1617226	total: 623ms	remaining: 552ms
265:	learn: 0.1611763	total: 625ms	remaining: 550ms
266:	learn: 0.1604988	total: 627ms	remaining: 547ms
267:	learn: 0.1599563	total: 629ms	remaining: 545ms
268:	learn: 0.1592211	total: 631ms	remaining: 542ms
269:	learn: 0.1587102	total: 634ms	remaining: 540ms
270:	learn: 0.1580619	total: 636ms	remaining: 537ms
271:	learn: 0.1577154	total: 639ms	remaining: 535ms
272:	learn: 0.1570859	total: 641ms	remaining: 533ms
273:	learn: 0.1564252	total: 643ms	remaining: 530ms
274:	learn: 0.1556874	total: 645ms	remaining: 528ms
275:	learn: 0.1551352	total: 648ms	remaining: 526ms
276:	learn: 0.1545568	total: 650ms	remaining: 523ms
277:	learn: 0.1540950	total: 652ms	remaining: 521ms
278:	learn: 0.1537386	total: 654ms	remaining: 518ms
279:	learn: 0.1531038	total: 657ms	remaining: 516ms
280:	learn: 0.1524487	total: 659ms	remaining: 514ms
281:	learn: 0.1518513	total: 661ms	remaining: 511ms
282:	learn: 0.1511052	total: 664ms	remaining: 509ms
283:	learn: 0.1504941	total: 666ms	remaining: 506ms
284:	learn: 0.1499178	total: 668ms	remaining: 504ms
285:	learn: 0.1492834	total: 670ms	remaining: 502ms
286:	learn: 0.1488692	total: 673ms	remaining: 499ms
287:	learn: 0.1482346	total: 675ms	remaining: 497ms
288:	learn: 0.1476973	total: 677ms	remaining: 495ms
289:	learn: 0.1471081	total: 680ms	remaining: 492ms
290:	learn: 0.1466836	total: 682ms	remaining: 490ms
291:	learn: 0.1462498	total: 685ms	remaining: 488ms
292:	learn: 0.1456462	total: 687ms	remaining: 485ms
293:	learn: 0.1451881	total: 689ms	remaining: 483ms
294:	learn: 0.1445972	total: 691ms	remaining: 481ms
295:	learn: 0.1441542	total: 694ms	remaining: 478ms
296:	learn: 0.1434395	total: 696ms	remaining: 476ms
297:	learn: 0.1429935	total: 698ms	remaining: 473ms
298:	learn: 0.1424935	total: 700ms	remaining: 471ms
299:	learn: 0.1420646	total: 703ms	remaining: 469ms
300:	learn: 0.1414503	total: 705ms	remaining: 466ms
301:	learn: 0.1410307	total: 707ms	remaining: 464ms
302:	learn: 0.1405834	total: 710ms	remaining: 461ms
303:	learn: 0.1400939	total: 712ms	remaining: 459ms
304:	learn: 0.1395127	total: 714ms	remaining: 457ms
305:	learn: 0.1389639	total: 716ms	remaining: 454ms
306:	learn: 0.1385120	total: 719ms	remaining: 452ms
307:	learn: 0.1378839	total: 721ms	remaining: 450ms
308:	learn: 0.1373315	total: 724ms	remaining: 447ms
309:	learn: 0.1366355	total: 726ms	remaining: 445ms
310:	learn: 0.1361482	total: 728ms	remaining: 443ms
311:	learn: 0.1357068	total: 731ms	remaining: 440ms
312:	learn: 0.1351990	total: 736ms	remaining: 440ms
313:	learn: 0.1347961	total: 738ms	remaining: 437ms
314:	learn: 0.1342762	total: 741ms	remaining: 435ms
315:	learn: 0.1338134	total: 743ms	remaining: 433ms
316:	learn: 0.1332989	total: 745ms	remaining: 430ms
317:	learn: 0.1329632	total: 748ms	remaining: 428ms
318:	learn: 0.1325441	total: 750ms	remaining: 426ms
319:	learn: 0.1320171	total: 753ms	remaining: 423ms
320:	learn: 0.1316048	total: 755ms	remaining: 421ms
321:	learn: 0.1310002	total: 757ms	remaining: 418ms
322:	learn: 0.1304578	total: 759ms	remaining: 416ms
323:	learn: 0.1299044	total: 762ms	remaining: 414ms
324:	learn: 0.1293640	total: 764ms	remaining: 411ms
325:	learn: 0.1288445	total: 766ms	remaining: 409ms
326:	learn: 0.1284046	total: 769ms	remaining: 407ms
327:	learn: 0.1279733	total: 771ms	remaining: 404ms
328:	learn: 0.1275441	total: 773ms	remaining: 402ms
329:	learn: 0.1272263	total: 776ms	remaining: 400ms
330:	learn: 0.1265064	total: 778ms	remaining: 397ms
331:	learn: 0.1260759	total: 780ms	remaining: 395ms
332:	learn: 0.1256873	total: 783ms	remaining: 393ms
333:	learn: 0.1253413	total: 785ms	remaining: 390ms
334:	learn: 0.1247913	total: 787ms	remaining: 388ms
335:	learn: 0.1243600	total: 790ms	remaining: 385ms
336:	learn: 0.1238527	total: 792ms	remaining: 383ms
337:	learn: 0.1233569	total: 794ms	remaining: 381ms
338:	learn: 0.1228303	total: 796ms	remaining: 378ms
339:	learn: 0.1223623	total: 799ms	remaining: 376ms
340:	learn: 0.1219933	total: 801ms	remaining: 373ms
341:	learn: 0.1217129	total: 803ms	remaining: 371ms
342:	learn: 0.1213273	total: 805ms	remaining: 369ms
343:	learn: 0.1208398	total: 808ms	remaining: 366ms
344:	learn: 0.1203083	total: 810ms	remaining: 364ms
345:	learn: 0.1199033	total: 812ms	remaining: 362ms
346:	learn: 0.1194650	total: 815ms	remaining: 359ms
347:	learn: 0.1190042	total: 817ms	remaining: 357ms
348:	learn: 0.1185422	total: 819ms	remaining: 354ms
349:	learn: 0.1181048	total: 821ms	remaining: 352ms
350:	learn: 0.1178337	total: 824ms	remaining: 350ms
351:	learn: 0.1173444	total: 826ms	remaining: 347ms
352:	learn: 0.1169826	total: 829ms	remaining: 345ms
353:	learn: 0.1166303	total: 832ms	remaining: 343ms
354:	learn: 0.1161574	total: 834ms	remaining: 341ms
355:	learn: 0.1158855	total: 837ms	remaining: 338ms
356:	learn: 0.1153349	total: 839ms	remaining: 336ms
357:	learn: 0.1149741	total: 841ms	remaining: 334ms
358:	learn: 0.1147259	total: 844ms	remaining: 331ms
359:	learn: 0.1142976	total: 846ms	remaining: 329ms
360:	learn: 0.1138179	total: 848ms	remaining: 327ms
361:	learn: 0.1135461	total: 850ms	remaining: 324ms
362:	learn: 0.1131870	total: 853ms	remaining: 322ms
363:	learn: 0.1128527	total: 855ms	remaining: 319ms
364:	learn: 0.1124492	total: 857ms	remaining: 317ms
365:	learn: 0.1121163	total: 860ms	remaining: 315ms
366:	learn: 0.1117505	total: 862ms	remaining: 312ms
367:	learn: 0.1112892	total: 864ms	remaining: 310ms
368:	learn: 0.1107245	total: 867ms	remaining: 308ms
369:	learn: 0.1102432	total: 869ms	remaining: 305ms
370:	learn: 0.1098692	total: 871ms	remaining: 303ms
371:	learn: 0.1095736	total: 873ms	remaining: 301ms
372:	learn: 0.1090944	total: 876ms	remaining: 298ms
373:	learn: 0.1085626	total: 878ms	remaining: 296ms
374:	learn: 0.1081613	total: 881ms	remaining: 294ms
375:	learn: 0.1076414	total: 883ms	remaining: 291ms
376:	learn: 0.1071751	total: 885ms	remaining: 289ms
377:	learn: 0.1068466	total: 888ms	remaining: 287ms
378:	learn: 0.1064856	total: 890ms	remaining: 284ms
379:	learn: 0.1060736	total: 892ms	remaining: 282ms
380:	learn: 0.1056714	total: 894ms	remaining: 279ms
381:	learn: 0.1053203	total: 897ms	remaining: 277ms
382:	learn: 0.1048077	total: 899ms	remaining: 275ms
383:	learn: 0.1044264	total: 901ms	remaining: 272ms
384:	learn: 0.1040809	total: 904ms	remaining: 270ms
385:	learn: 0.1036667	total: 906ms	remaining: 268ms
386:	learn: 0.1034040	total: 908ms	remaining: 265ms
387:	learn: 0.1030173	total: 911ms	remaining: 263ms
388:	learn: 0.1026221	total: 914ms	remaining: 261ms
389:	learn: 0.1022475	total: 916ms	remaining: 258ms
390:	learn: 0.1017831	total: 919ms	remaining: 256ms
391:	learn: 0.1013113	total: 921ms	remaining: 254ms
392:	learn: 0.1009822	total: 923ms	remaining: 251ms
393:	learn: 0.1007272	total: 928ms	remaining: 250ms
394:	learn: 0.1004105	total: 930ms	remaining: 247ms
395:	learn: 0.0999323	total: 932ms	remaining: 245ms
396:	learn: 0.0994480	total: 934ms	remaining: 242ms
397:	learn: 0.0990169	total: 937ms	remaining: 240ms
398:	learn: 0.0987014	total: 939ms	remaining: 238ms
399:	learn: 0.0983506	total: 942ms	remaining: 236ms
400:	learn: 0.0979478	total: 945ms	remaining: 233ms
401:	learn: 0.0977153	total: 947ms	remaining: 231ms
402:	learn: 0.0974027	total: 949ms	remaining: 229ms
403:	learn: 0.0970677	total: 952ms	remaining: 226ms
404:	learn: 0.0966419	total: 954ms	remaining: 224ms
405:	learn: 0.0962957	total: 956ms	remaining: 221ms
406:	learn: 0.0959864	total: 959ms	remaining: 219ms
407:	learn: 0.0955497	total: 961ms	remaining: 217ms
408:	learn: 0.0952573	total: 963ms	remaining: 214ms
409:	learn: 0.0949902	total: 965ms	remaining: 212ms
410:	learn: 0.0946321	total: 968ms	remaining: 210ms
411:	learn: 0.0941986	total: 970ms	remaining: 207ms
412:	learn: 0.0939482	total: 972ms	remaining: 205ms
413:	learn: 0.0936510	total: 974ms	remaining: 202ms
414:	learn: 0.0933622	total: 977ms	remaining: 200ms
415:	learn: 0.0930323	total: 979ms	remaining: 198ms
416:	learn: 0.0927563	total: 982ms	remaining: 195ms
417:	learn: 0.0924877	total: 984ms	remaining: 193ms
418:	learn: 0.0921539	total: 987ms	remaining: 191ms
419:	learn: 0.0918803	total: 989ms	remaining: 188ms
420:	learn: 0.0916034	total: 991ms	remaining: 186ms
421:	learn: 0.0913562	total: 993ms	remaining: 184ms
422:	learn: 0.0911153	total: 995ms	remaining: 181ms
423:	learn: 0.0908144	total: 998ms	remaining: 179ms
424:	learn: 0.0904650	total: 1000ms	remaining: 176ms
425:	learn: 0.0901516	total: 1s	remaining: 174ms
426:	learn: 0.0898573	total: 1s	remaining: 172ms
427:	learn: 0.0895570	total: 1.01s	remaining: 169ms
428:	learn: 0.0892577	total: 1.01s	remaining: 167ms
429:	learn: 0.0889699	total: 1.01s	remaining: 165ms
430:	learn: 0.0887706	total: 1.01s	remaining: 162ms
431:	learn: 0.0884887	total: 1.01s	remaining: 160ms
432:	learn: 0.0881867	total: 1.02s	remaining: 157ms
433:	learn: 0.0879621	total: 1.02s	remaining: 155ms
434:	learn: 0.0876723	total: 1.02s	remaining: 153ms
435:	learn: 0.0872819	total: 1.03s	remaining: 151ms
436:	learn: 0.0870103	total: 1.03s	remaining: 148ms
437:	learn: 0.0867043	total: 1.03s	remaining: 146ms
438:	learn: 0.0864819	total: 1.03s	remaining: 144ms
439:	learn: 0.0861811	total: 1.03s	remaining: 141ms
440:	learn: 0.0858872	total: 1.04s	remaining: 139ms
441:	learn: 0.0855791	total: 1.04s	remaining: 136ms
442:	learn: 0.0853367	total: 1.04s	remaining: 134ms
443:	learn: 0.0850399	total: 1.04s	remaining: 132ms
444:	learn: 0.0848162	total: 1.05s	remaining: 129ms
445:	learn: 0.0845585	total: 1.05s	remaining: 127ms
446:	learn: 0.0843026	total: 1.05s	remaining: 125ms
447:	learn: 0.0840758	total: 1.05s	remaining: 122ms
448:	learn: 0.0836951	total: 1.05s	remaining: 120ms
449:	learn: 0.0833411	total: 1.06s	remaining: 118ms
450:	learn: 0.0831177	total: 1.06s	remaining: 115ms
451:	learn: 0.0828528	total: 1.06s	remaining: 113ms
452:	learn: 0.0826025	total: 1.06s	remaining: 111ms
453:	learn: 0.0824252	total: 1.07s	remaining: 108ms
454:	learn: 0.0820657	total: 1.07s	remaining: 106ms
455:	learn: 0.0818515	total: 1.07s	remaining: 103ms
456:	learn: 0.0815504	total: 1.07s	remaining: 101ms
457:	learn: 0.0812380	total: 1.08s	remaining: 98.8ms
458:	learn: 0.0810457	total: 1.08s	remaining: 96.4ms
459:	learn: 0.0808117	total: 1.08s	remaining: 94.1ms
460:	learn: 0.0805745	total: 1.08s	remaining: 91.7ms
461:	learn: 0.0803685	total: 1.09s	remaining: 89.3ms
462:	learn: 0.0800976	total: 1.09s	remaining: 87ms
463:	learn: 0.0799066	total: 1.09s	remaining: 84.6ms
464:	learn: 0.0797027	total: 1.09s	remaining: 82.3ms
465:	learn: 0.0794606	total: 1.09s	remaining: 79.9ms
466:	learn: 0.0791164	total: 1.1s	remaining: 77.6ms
467:	learn: 0.0788206	total: 1.1s	remaining: 75.2ms
468:	learn: 0.0785634	total: 1.1s	remaining: 72.9ms
469:	learn: 0.0783369	total: 1.1s	remaining: 70.5ms
470:	learn: 0.0781536	total: 1.11s	remaining: 68.1ms
471:	learn: 0.0779236	total: 1.11s	remaining: 65.8ms
472:	learn: 0.0776388	total: 1.11s	remaining: 63.4ms
473:	learn: 0.0773672	total: 1.11s	remaining: 61.1ms
474:	learn: 0.0771605	total: 1.11s	remaining: 58.7ms
475:	learn: 0.0769234	total: 1.12s	remaining: 56.6ms
476:	learn: 0.0767171	total: 1.12s	remaining: 54.2ms
477:	learn: 0.0764977	total: 1.13s	remaining: 51.9ms
478:	learn: 0.0762102	total: 1.13s	remaining: 49.5ms
479:	learn: 0.0758698	total: 1.13s	remaining: 47.1ms
480:	learn: 0.0756801	total: 1.13s	remaining: 44.8ms
481:	learn: 0.0753759	total: 1.14s	remaining: 42.4ms
482:	learn: 0.0751689	total: 1.14s	remaining: 40.1ms
483:	learn: 0.0748100	total: 1.14s	remaining: 37.7ms
484:	learn: 0.0745940	total: 1.14s	remaining: 35.3ms
485:	learn: 0.0743195	total: 1.15s	remaining: 33ms
486:	learn: 0.0740652	total: 1.15s	remaining: 30.6ms
487:	learn: 0.0739427	total: 1.15s	remaining: 28.3ms
488:	learn: 0.0736950	total: 1.15s	remaining: 25.9ms
489:	learn: 0.0733647	total: 1.15s	remaining: 23.6ms
490:	learn: 0.0730284	total: 1.16s	remaining: 21.2ms
491:	learn: 0.0727697	total: 1.16s	remaining: 18.8ms
492:	learn: 0.0725412	total: 1.16s	remaining: 16.5ms
493:	learn: 0.0723344	total: 1.16s	remaining: 14.1ms
494:	learn: 0.0720322	total: 1.17s	remaining: 11.8ms
495:	learn: 0.0718516	total: 1.17s	remaining: 9.42ms
496:	learn: 0.0716128	total: 1.17s	remaining: 7.07ms
497:	learn: 0.0713694	total: 1.17s	remaining: 4.71ms
498:	learn: 0.0711401	total: 1.18s	remaining: 2.35ms
499:	learn: 0.0708974	total: 1.18s	remaining: 0us
0:	learn: 0.6884562	total: 3.35ms	remaining: 1.67s
1:	learn: 0.6822377	total: 5.69ms	remaining: 1.42s
2:	learn: 0.6781519	total: 7.97ms	remaining: 1.32s
3:	learn: 0.6716880	total: 10.2ms	remaining: 1.27s
4:	learn: 0.6656099	total: 12.5ms	remaining: 1.24s
5:	learn: 0.6612259	total: 14.8ms	remaining: 1.22s
6:	learn: 0.6556750	total: 17.1ms	remaining: 1.2s
7:	learn: 0.6506561	total: 19.3ms	remaining: 1.19s
8:	learn: 0.6435695	total: 21.6ms	remaining: 1.18s
9:	learn: 0.6385241	total: 23.8ms	remaining: 1.17s
10:	learn: 0.6327053	total: 26.1ms	remaining: 1.16s
11:	learn: 0.6278030	total: 28.3ms	remaining: 1.15s
12:	learn: 0.6242023	total: 30.5ms	remaining: 1.14s
13:	learn: 0.6188283	total: 32.8ms	remaining: 1.14s
14:	learn: 0.6139085	total: 35.1ms	remaining: 1.13s
15:	learn: 0.6101297	total: 37.3ms	remaining: 1.13s
16:	learn: 0.6040287	total: 39.6ms	remaining: 1.12s
17:	learn: 0.5980113	total: 41.9ms	remaining: 1.12s
18:	learn: 0.5936445	total: 44.2ms	remaining: 1.12s
19:	learn: 0.5896393	total: 46.7ms	remaining: 1.12s
20:	learn: 0.5843945	total: 49ms	remaining: 1.12s
21:	learn: 0.5795483	total: 51.2ms	remaining: 1.11s
22:	learn: 0.5749898	total: 53.6ms	remaining: 1.11s
23:	learn: 0.5704080	total: 60ms	remaining: 1.19s
24:	learn: 0.5650529	total: 62.2ms	remaining: 1.18s
25:	learn: 0.5618949	total: 64.6ms	remaining: 1.18s
26:	learn: 0.5571543	total: 67ms	remaining: 1.17s
27:	learn: 0.5528593	total: 69.4ms	remaining: 1.17s
28:	learn: 0.5490551	total: 71.7ms	remaining: 1.16s
29:	learn: 0.5454081	total: 73.9ms	remaining: 1.16s
30:	learn: 0.5425841	total: 76.1ms	remaining: 1.15s
31:	learn: 0.5390856	total: 78.4ms	remaining: 1.15s
32:	learn: 0.5342585	total: 80.6ms	remaining: 1.14s
33:	learn: 0.5302129	total: 82.9ms	remaining: 1.14s
34:	learn: 0.5264513	total: 85.1ms	remaining: 1.13s
35:	learn: 0.5234519	total: 87.5ms	remaining: 1.13s
36:	learn: 0.5203045	total: 89.7ms	remaining: 1.12s
37:	learn: 0.5166766	total: 92.1ms	remaining: 1.12s
38:	learn: 0.5133135	total: 94.3ms	remaining: 1.11s
39:	learn: 0.5097089	total: 96.6ms	remaining: 1.11s
40:	learn: 0.5062652	total: 98.9ms	remaining: 1.11s
41:	learn: 0.5026867	total: 101ms	remaining: 1.1s
42:	learn: 0.4987176	total: 103ms	remaining: 1.1s
43:	learn: 0.4949921	total: 106ms	remaining: 1.09s
44:	learn: 0.4919830	total: 108ms	remaining: 1.09s
45:	learn: 0.4891091	total: 110ms	remaining: 1.09s
46:	learn: 0.4838286	total: 113ms	remaining: 1.08s
47:	learn: 0.4792745	total: 115ms	remaining: 1.08s
48:	learn: 0.4753300	total: 117ms	remaining: 1.08s
49:	learn: 0.4717879	total: 119ms	remaining: 1.07s
50:	learn: 0.4676785	total: 122ms	remaining: 1.07s
51:	learn: 0.4644070	total: 124ms	remaining: 1.07s
52:	learn: 0.4615792	total: 127ms	remaining: 1.07s
53:	learn: 0.4585614	total: 129ms	remaining: 1.06s
54:	learn: 0.4554360	total: 131ms	remaining: 1.06s
55:	learn: 0.4526494	total: 133ms	remaining: 1.06s
56:	learn: 0.4499258	total: 136ms	remaining: 1.05s
57:	learn: 0.4464671	total: 138ms	remaining: 1.05s
58:	learn: 0.4429250	total: 140ms	remaining: 1.05s
59:	learn: 0.4401404	total: 143ms	remaining: 1.05s
60:	learn: 0.4372954	total: 145ms	remaining: 1.05s
61:	learn: 0.4343742	total: 148ms	remaining: 1.04s
62:	learn: 0.4310275	total: 150ms	remaining: 1.04s
63:	learn: 0.4271912	total: 152ms	remaining: 1.04s
64:	learn: 0.4231567	total: 155ms	remaining: 1.03s
65:	learn: 0.4196869	total: 157ms	remaining: 1.03s
66:	learn: 0.4169252	total: 159ms	remaining: 1.03s
67:	learn: 0.4138418	total: 161ms	remaining: 1.02s
68:	learn: 0.4108896	total: 164ms	remaining: 1.02s
69:	learn: 0.4084268	total: 166ms	remaining: 1.02s
70:	learn: 0.4058282	total: 168ms	remaining: 1.02s
71:	learn: 0.4029258	total: 171ms	remaining: 1.01s
72:	learn: 0.4003848	total: 173ms	remaining: 1.01s
73:	learn: 0.3980519	total: 175ms	remaining: 1.01s
74:	learn: 0.3950994	total: 177ms	remaining: 1s
75:	learn: 0.3913900	total: 180ms	remaining: 1s
76:	learn: 0.3887870	total: 182ms	remaining: 999ms
77:	learn: 0.3863044	total: 184ms	remaining: 997ms
78:	learn: 0.3839919	total: 187ms	remaining: 995ms
79:	learn: 0.3818758	total: 189ms	remaining: 993ms
80:	learn: 0.3791598	total: 192ms	remaining: 995ms
81:	learn: 0.3765956	total: 195ms	remaining: 992ms
82:	learn: 0.3743868	total: 197ms	remaining: 989ms
83:	learn: 0.3717199	total: 199ms	remaining: 986ms
84:	learn: 0.3696184	total: 201ms	remaining: 984ms
85:	learn: 0.3669647	total: 204ms	remaining: 981ms
86:	learn: 0.3648306	total: 206ms	remaining: 979ms
87:	learn: 0.3628739	total: 208ms	remaining: 976ms
88:	learn: 0.3603391	total: 211ms	remaining: 974ms
89:	learn: 0.3590227	total: 213ms	remaining: 971ms
90:	learn: 0.3570430	total: 215ms	remaining: 968ms
91:	learn: 0.3542772	total: 218ms	remaining: 965ms
92:	learn: 0.3520278	total: 220ms	remaining: 963ms
93:	learn: 0.3499845	total: 222ms	remaining: 960ms
94:	learn: 0.3481220	total: 225ms	remaining: 958ms
95:	learn: 0.3462478	total: 227ms	remaining: 956ms
96:	learn: 0.3443569	total: 230ms	remaining: 954ms
97:	learn: 0.3423846	total: 232ms	remaining: 951ms
98:	learn: 0.3409375	total: 234ms	remaining: 949ms
99:	learn: 0.3382015	total: 236ms	remaining: 946ms
100:	learn: 0.3364590	total: 239ms	remaining: 944ms
101:	learn: 0.3346914	total: 242ms	remaining: 943ms
102:	learn: 0.3329470	total: 244ms	remaining: 941ms
103:	learn: 0.3308943	total: 246ms	remaining: 938ms
104:	learn: 0.3288445	total: 249ms	remaining: 936ms
105:	learn: 0.3266675	total: 251ms	remaining: 933ms
106:	learn: 0.3249440	total: 253ms	remaining: 931ms
107:	learn: 0.3232557	total: 256ms	remaining: 928ms
108:	learn: 0.3215742	total: 258ms	remaining: 925ms
109:	learn: 0.3197934	total: 260ms	remaining: 922ms
110:	learn: 0.3180125	total: 263ms	remaining: 920ms
111:	learn: 0.3162685	total: 265ms	remaining: 917ms
112:	learn: 0.3147657	total: 267ms	remaining: 915ms
113:	learn: 0.3128023	total: 269ms	remaining: 912ms
114:	learn: 0.3108461	total: 272ms	remaining: 910ms
115:	learn: 0.3095582	total: 274ms	remaining: 907ms
116:	learn: 0.3077072	total: 276ms	remaining: 904ms
117:	learn: 0.3059529	total: 278ms	remaining: 901ms
118:	learn: 0.3046462	total: 281ms	remaining: 901ms
119:	learn: 0.3029753	total: 284ms	remaining: 898ms
120:	learn: 0.3010294	total: 286ms	remaining: 896ms
121:	learn: 0.2997962	total: 288ms	remaining: 893ms
122:	learn: 0.2974814	total: 291ms	remaining: 891ms
123:	learn: 0.2962822	total: 293ms	remaining: 888ms
124:	learn: 0.2948324	total: 295ms	remaining: 886ms
125:	learn: 0.2932735	total: 297ms	remaining: 883ms
126:	learn: 0.2916996	total: 300ms	remaining: 880ms
127:	learn: 0.2906392	total: 302ms	remaining: 878ms
128:	learn: 0.2894461	total: 304ms	remaining: 875ms
129:	learn: 0.2879031	total: 306ms	remaining: 872ms
130:	learn: 0.2864936	total: 309ms	remaining: 870ms
131:	learn: 0.2850563	total: 311ms	remaining: 868ms
132:	learn: 0.2835942	total: 314ms	remaining: 865ms
133:	learn: 0.2818049	total: 316ms	remaining: 863ms
134:	learn: 0.2805950	total: 318ms	remaining: 860ms
135:	learn: 0.2793065	total: 321ms	remaining: 858ms
136:	learn: 0.2777701	total: 323ms	remaining: 856ms
137:	learn: 0.2765188	total: 325ms	remaining: 853ms
138:	learn: 0.2748876	total: 328ms	remaining: 851ms
139:	learn: 0.2730982	total: 330ms	remaining: 848ms
140:	learn: 0.2719374	total: 332ms	remaining: 846ms
141:	learn: 0.2706956	total: 334ms	remaining: 843ms
142:	learn: 0.2698406	total: 337ms	remaining: 840ms
143:	learn: 0.2684440	total: 339ms	remaining: 838ms
144:	learn: 0.2667939	total: 341ms	remaining: 835ms
145:	learn: 0.2657520	total: 343ms	remaining: 833ms
146:	learn: 0.2648977	total: 346ms	remaining: 830ms
147:	learn: 0.2637522	total: 348ms	remaining: 828ms
148:	learn: 0.2626183	total: 350ms	remaining: 825ms
149:	learn: 0.2613425	total: 353ms	remaining: 823ms
150:	learn: 0.2598442	total: 355ms	remaining: 820ms
151:	learn: 0.2585792	total: 357ms	remaining: 818ms
152:	learn: 0.2573525	total: 360ms	remaining: 816ms
153:	learn: 0.2561790	total: 362ms	remaining: 813ms
154:	learn: 0.2546078	total: 364ms	remaining: 810ms
155:	learn: 0.2537550	total: 366ms	remaining: 808ms
156:	learn: 0.2526279	total: 369ms	remaining: 806ms
157:	learn: 0.2518018	total: 371ms	remaining: 803ms
158:	learn: 0.2504153	total: 373ms	remaining: 801ms
159:	learn: 0.2492613	total: 376ms	remaining: 798ms
160:	learn: 0.2484261	total: 380ms	remaining: 800ms
161:	learn: 0.2473156	total: 383ms	remaining: 800ms
162:	learn: 0.2461006	total: 386ms	remaining: 797ms
163:	learn: 0.2448066	total: 388ms	remaining: 795ms
164:	learn: 0.2438587	total: 390ms	remaining: 792ms
165:	learn: 0.2428343	total: 393ms	remaining: 790ms
166:	learn: 0.2418023	total: 395ms	remaining: 787ms
167:	learn: 0.2407361	total: 397ms	remaining: 785ms
168:	learn: 0.2400107	total: 399ms	remaining: 782ms
169:	learn: 0.2387520	total: 402ms	remaining: 780ms
170:	learn: 0.2375060	total: 404ms	remaining: 778ms
171:	learn: 0.2360809	total: 406ms	remaining: 775ms
172:	learn: 0.2349951	total: 409ms	remaining: 773ms
173:	learn: 0.2341581	total: 411ms	remaining: 770ms
174:	learn: 0.2330148	total: 413ms	remaining: 768ms
175:	learn: 0.2317752	total: 415ms	remaining: 765ms
176:	learn: 0.2308105	total: 418ms	remaining: 763ms
177:	learn: 0.2298824	total: 420ms	remaining: 760ms
178:	learn: 0.2282856	total: 422ms	remaining: 758ms
179:	learn: 0.2268897	total: 425ms	remaining: 755ms
180:	learn: 0.2257469	total: 427ms	remaining: 753ms
181:	learn: 0.2245994	total: 429ms	remaining: 750ms
182:	learn: 0.2235550	total: 432ms	remaining: 748ms
183:	learn: 0.2227417	total: 434ms	remaining: 746ms
184:	learn: 0.2221200	total: 437ms	remaining: 744ms
185:	learn: 0.2212012	total: 439ms	remaining: 742ms
186:	learn: 0.2203374	total: 442ms	remaining: 739ms
187:	learn: 0.2190160	total: 446ms	remaining: 740ms
188:	learn: 0.2176859	total: 448ms	remaining: 737ms
189:	learn: 0.2167354	total: 450ms	remaining: 735ms
190:	learn: 0.2157945	total: 453ms	remaining: 732ms
191:	learn: 0.2149590	total: 455ms	remaining: 730ms
192:	learn: 0.2140016	total: 457ms	remaining: 728ms
193:	learn: 0.2130931	total: 460ms	remaining: 725ms
194:	learn: 0.2122432	total: 462ms	remaining: 722ms
195:	learn: 0.2114617	total: 464ms	remaining: 720ms
196:	learn: 0.2106608	total: 466ms	remaining: 717ms
197:	learn: 0.2097170	total: 469ms	remaining: 715ms
198:	learn: 0.2088859	total: 471ms	remaining: 712ms
199:	learn: 0.2079649	total: 475ms	remaining: 713ms
200:	learn: 0.2071560	total: 478ms	remaining: 711ms
201:	learn: 0.2064045	total: 480ms	remaining: 708ms
202:	learn: 0.2054850	total: 482ms	remaining: 705ms
203:	learn: 0.2047434	total: 484ms	remaining: 703ms
204:	learn: 0.2041212	total: 487ms	remaining: 700ms
205:	learn: 0.2033477	total: 489ms	remaining: 698ms
206:	learn: 0.2024314	total: 491ms	remaining: 695ms
207:	learn: 0.2015550	total: 493ms	remaining: 693ms
208:	learn: 0.2006806	total: 496ms	remaining: 690ms
209:	learn: 0.2000116	total: 498ms	remaining: 688ms
210:	learn: 0.1992107	total: 500ms	remaining: 685ms
211:	learn: 0.1984727	total: 502ms	remaining: 682ms
212:	learn: 0.1974693	total: 505ms	remaining: 680ms
213:	learn: 0.1966626	total: 507ms	remaining: 677ms
214:	learn: 0.1960735	total: 509ms	remaining: 675ms
215:	learn: 0.1950961	total: 511ms	remaining: 672ms
216:	learn: 0.1943285	total: 514ms	remaining: 670ms
217:	learn: 0.1934084	total: 516ms	remaining: 667ms
218:	learn: 0.1925720	total: 518ms	remaining: 665ms
219:	learn: 0.1918144	total: 521ms	remaining: 663ms
220:	learn: 0.1912192	total: 523ms	remaining: 660ms
221:	learn: 0.1904431	total: 525ms	remaining: 658ms
222:	learn: 0.1898163	total: 528ms	remaining: 655ms
223:	learn: 0.1893118	total: 530ms	remaining: 653ms
224:	learn: 0.1881324	total: 532ms	remaining: 651ms
225:	learn: 0.1874561	total: 535ms	remaining: 648ms
226:	learn: 0.1866702	total: 537ms	remaining: 646ms
227:	learn: 0.1857833	total: 539ms	remaining: 643ms
228:	learn: 0.1849335	total: 542ms	remaining: 641ms
229:	learn: 0.1840257	total: 544ms	remaining: 639ms
230:	learn: 0.1832885	total: 546ms	remaining: 636ms
231:	learn: 0.1822870	total: 548ms	remaining: 634ms
232:	learn: 0.1813986	total: 551ms	remaining: 631ms
233:	learn: 0.1804538	total: 553ms	remaining: 629ms
234:	learn: 0.1796259	total: 555ms	remaining: 626ms
235:	learn: 0.1788229	total: 558ms	remaining: 624ms
236:	learn: 0.1783131	total: 560ms	remaining: 622ms
237:	learn: 0.1776827	total: 562ms	remaining: 619ms
238:	learn: 0.1770268	total: 565ms	remaining: 617ms
239:	learn: 0.1764584	total: 567ms	remaining: 614ms
240:	learn: 0.1755700	total: 569ms	remaining: 612ms
241:	learn: 0.1749868	total: 573ms	remaining: 611ms
242:	learn: 0.1743861	total: 575ms	remaining: 608ms
243:	learn: 0.1735682	total: 577ms	remaining: 606ms
244:	learn: 0.1726311	total: 580ms	remaining: 603ms
245:	learn: 0.1719206	total: 582ms	remaining: 601ms
246:	learn: 0.1712231	total: 584ms	remaining: 598ms
247:	learn: 0.1703211	total: 587ms	remaining: 597ms
248:	learn: 0.1696354	total: 590ms	remaining: 595ms
249:	learn: 0.1689423	total: 592ms	remaining: 592ms
250:	learn: 0.1684750	total: 595ms	remaining: 590ms
251:	learn: 0.1680326	total: 597ms	remaining: 588ms
252:	learn: 0.1674289	total: 599ms	remaining: 585ms
253:	learn: 0.1669646	total: 602ms	remaining: 583ms
254:	learn: 0.1662678	total: 604ms	remaining: 581ms
255:	learn: 0.1657366	total: 607ms	remaining: 578ms
256:	learn: 0.1651637	total: 609ms	remaining: 576ms
257:	learn: 0.1645988	total: 612ms	remaining: 574ms
258:	learn: 0.1639972	total: 614ms	remaining: 571ms
259:	learn: 0.1635440	total: 617ms	remaining: 569ms
260:	learn: 0.1627778	total: 619ms	remaining: 567ms
261:	learn: 0.1622116	total: 621ms	remaining: 565ms
262:	learn: 0.1615825	total: 624ms	remaining: 562ms
263:	learn: 0.1609906	total: 626ms	remaining: 560ms
264:	learn: 0.1602094	total: 630ms	remaining: 558ms
265:	learn: 0.1593802	total: 632ms	remaining: 556ms
266:	learn: 0.1588416	total: 635ms	remaining: 554ms
267:	learn: 0.1582958	total: 637ms	remaining: 551ms
268:	learn: 0.1576687	total: 639ms	remaining: 549ms
269:	learn: 0.1566505	total: 642ms	remaining: 547ms
270:	learn: 0.1560422	total: 644ms	remaining: 544ms
271:	learn: 0.1555121	total: 647ms	remaining: 542ms
272:	learn: 0.1550435	total: 649ms	remaining: 540ms
273:	learn: 0.1544223	total: 652ms	remaining: 538ms
274:	learn: 0.1536974	total: 654ms	remaining: 535ms
275:	learn: 0.1530294	total: 657ms	remaining: 533ms
276:	learn: 0.1524378	total: 659ms	remaining: 531ms
277:	learn: 0.1519961	total: 662ms	remaining: 528ms
278:	learn: 0.1513822	total: 664ms	remaining: 526ms
279:	learn: 0.1509851	total: 666ms	remaining: 524ms
280:	learn: 0.1504003	total: 670ms	remaining: 522ms
281:	learn: 0.1497219	total: 672ms	remaining: 520ms
282:	learn: 0.1491552	total: 674ms	remaining: 517ms
283:	learn: 0.1484571	total: 677ms	remaining: 515ms
284:	learn: 0.1477355	total: 679ms	remaining: 512ms
285:	learn: 0.1470131	total: 681ms	remaining: 510ms
286:	learn: 0.1465899	total: 683ms	remaining: 507ms
287:	learn: 0.1459078	total: 686ms	remaining: 505ms
288:	learn: 0.1453710	total: 688ms	remaining: 502ms
289:	learn: 0.1448142	total: 690ms	remaining: 500ms
290:	learn: 0.1443422	total: 693ms	remaining: 498ms
291:	learn: 0.1439070	total: 695ms	remaining: 495ms
292:	learn: 0.1434766	total: 697ms	remaining: 493ms
293:	learn: 0.1428635	total: 700ms	remaining: 490ms
294:	learn: 0.1422986	total: 702ms	remaining: 488ms
295:	learn: 0.1418880	total: 704ms	remaining: 485ms
296:	learn: 0.1412154	total: 707ms	remaining: 483ms
297:	learn: 0.1407861	total: 709ms	remaining: 481ms
298:	learn: 0.1404262	total: 711ms	remaining: 478ms
299:	learn: 0.1397737	total: 713ms	remaining: 476ms
300:	learn: 0.1391388	total: 716ms	remaining: 473ms
301:	learn: 0.1384077	total: 718ms	remaining: 471ms
302:	learn: 0.1379775	total: 720ms	remaining: 468ms
303:	learn: 0.1374202	total: 723ms	remaining: 466ms
304:	learn: 0.1368032	total: 725ms	remaining: 464ms
305:	learn: 0.1360344	total: 727ms	remaining: 461ms
306:	learn: 0.1355719	total: 730ms	remaining: 459ms
307:	learn: 0.1350637	total: 732ms	remaining: 456ms
308:	learn: 0.1345454	total: 734ms	remaining: 454ms
309:	learn: 0.1341281	total: 737ms	remaining: 452ms
310:	learn: 0.1334762	total: 739ms	remaining: 449ms
311:	learn: 0.1329600	total: 741ms	remaining: 447ms
312:	learn: 0.1325111	total: 744ms	remaining: 444ms
313:	learn: 0.1321052	total: 746ms	remaining: 442ms
314:	learn: 0.1314986	total: 748ms	remaining: 440ms
315:	learn: 0.1310491	total: 751ms	remaining: 437ms
316:	learn: 0.1303388	total: 753ms	remaining: 435ms
317:	learn: 0.1299258	total: 755ms	remaining: 432ms
318:	learn: 0.1292860	total: 758ms	remaining: 430ms
319:	learn: 0.1290398	total: 760ms	remaining: 428ms
320:	learn: 0.1287771	total: 763ms	remaining: 425ms
321:	learn: 0.1283429	total: 767ms	remaining: 424ms
322:	learn: 0.1281158	total: 770ms	remaining: 422ms
323:	learn: 0.1274384	total: 772ms	remaining: 419ms
324:	learn: 0.1269546	total: 775ms	remaining: 417ms
325:	learn: 0.1264091	total: 777ms	remaining: 415ms
326:	learn: 0.1259336	total: 779ms	remaining: 412ms
327:	learn: 0.1255475	total: 782ms	remaining: 410ms
328:	learn: 0.1250827	total: 784ms	remaining: 408ms
329:	learn: 0.1247408	total: 787ms	remaining: 405ms
330:	learn: 0.1243148	total: 790ms	remaining: 404ms
331:	learn: 0.1238443	total: 793ms	remaining: 401ms
332:	learn: 0.1231415	total: 795ms	remaining: 399ms
333:	learn: 0.1227951	total: 798ms	remaining: 397ms
334:	learn: 0.1224914	total: 800ms	remaining: 394ms
335:	learn: 0.1220117	total: 803ms	remaining: 392ms
336:	learn: 0.1214481	total: 805ms	remaining: 389ms
337:	learn: 0.1210398	total: 808ms	remaining: 387ms
338:	learn: 0.1206174	total: 810ms	remaining: 385ms
339:	learn: 0.1202604	total: 812ms	remaining: 382ms
340:	learn: 0.1199511	total: 815ms	remaining: 380ms
341:	learn: 0.1197182	total: 817ms	remaining: 378ms
342:	learn: 0.1193723	total: 820ms	remaining: 375ms
343:	learn: 0.1190327	total: 823ms	remaining: 373ms
344:	learn: 0.1185441	total: 826ms	remaining: 371ms
345:	learn: 0.1180863	total: 828ms	remaining: 369ms
346:	learn: 0.1177436	total: 831ms	remaining: 366ms
347:	learn: 0.1173486	total: 833ms	remaining: 364ms
348:	learn: 0.1168208	total: 835ms	remaining: 361ms
349:	learn: 0.1163235	total: 837ms	remaining: 359ms
350:	learn: 0.1160359	total: 840ms	remaining: 356ms
351:	learn: 0.1156242	total: 842ms	remaining: 354ms
352:	learn: 0.1152296	total: 844ms	remaining: 352ms
353:	learn: 0.1146880	total: 847ms	remaining: 349ms
354:	learn: 0.1142408	total: 849ms	remaining: 347ms
355:	learn: 0.1139551	total: 851ms	remaining: 344ms
356:	learn: 0.1134782	total: 853ms	remaining: 342ms
357:	learn: 0.1131605	total: 856ms	remaining: 339ms
358:	learn: 0.1128633	total: 858ms	remaining: 337ms
359:	learn: 0.1124443	total: 862ms	remaining: 335ms
360:	learn: 0.1119479	total: 864ms	remaining: 333ms
361:	learn: 0.1116306	total: 867ms	remaining: 330ms
362:	learn: 0.1112569	total: 869ms	remaining: 328ms
363:	learn: 0.1110344	total: 871ms	remaining: 326ms
364:	learn: 0.1107285	total: 873ms	remaining: 323ms
365:	learn: 0.1103254	total: 876ms	remaining: 321ms
366:	learn: 0.1100575	total: 878ms	remaining: 318ms
367:	learn: 0.1096220	total: 881ms	remaining: 316ms
368:	learn: 0.1092729	total: 883ms	remaining: 313ms
369:	learn: 0.1089151	total: 885ms	remaining: 311ms
370:	learn: 0.1085386	total: 888ms	remaining: 309ms
371:	learn: 0.1081805	total: 890ms	remaining: 306ms
372:	learn: 0.1077357	total: 892ms	remaining: 304ms
373:	learn: 0.1072858	total: 895ms	remaining: 301ms
374:	learn: 0.1070034	total: 897ms	remaining: 299ms
375:	learn: 0.1066167	total: 899ms	remaining: 297ms
376:	learn: 0.1062157	total: 903ms	remaining: 295ms
377:	learn: 0.1058083	total: 905ms	remaining: 292ms
378:	learn: 0.1055382	total: 907ms	remaining: 290ms
379:	learn: 0.1052715	total: 910ms	remaining: 287ms
380:	learn: 0.1048233	total: 912ms	remaining: 285ms
381:	learn: 0.1044133	total: 914ms	remaining: 282ms
382:	learn: 0.1039866	total: 916ms	remaining: 280ms
383:	learn: 0.1036247	total: 919ms	remaining: 278ms
384:	learn: 0.1031731	total: 921ms	remaining: 275ms
385:	learn: 0.1028235	total: 924ms	remaining: 273ms
386:	learn: 0.1025169	total: 926ms	remaining: 270ms
387:	learn: 0.1021953	total: 928ms	remaining: 268ms
388:	learn: 0.1018214	total: 930ms	remaining: 266ms
389:	learn: 0.1014883	total: 933ms	remaining: 263ms
390:	learn: 0.1011163	total: 935ms	remaining: 261ms
391:	learn: 0.1007933	total: 937ms	remaining: 258ms
392:	learn: 0.1004519	total: 940ms	remaining: 256ms
393:	learn: 0.1001698	total: 942ms	remaining: 254ms
394:	learn: 0.0998223	total: 945ms	remaining: 251ms
395:	learn: 0.0993342	total: 947ms	remaining: 249ms
396:	learn: 0.0989487	total: 949ms	remaining: 246ms
397:	learn: 0.0986472	total: 952ms	remaining: 244ms
398:	learn: 0.0982816	total: 954ms	remaining: 242ms
399:	learn: 0.0978404	total: 959ms	remaining: 240ms
400:	learn: 0.0973560	total: 961ms	remaining: 237ms
401:	learn: 0.0970144	total: 964ms	remaining: 235ms
402:	learn: 0.0965856	total: 966ms	remaining: 233ms
403:	learn: 0.0962697	total: 969ms	remaining: 230ms
404:	learn: 0.0960104	total: 971ms	remaining: 228ms
405:	learn: 0.0957204	total: 974ms	remaining: 225ms
406:	learn: 0.0955184	total: 976ms	remaining: 223ms
407:	learn: 0.0952761	total: 978ms	remaining: 221ms
408:	learn: 0.0949954	total: 981ms	remaining: 218ms
409:	learn: 0.0947627	total: 983ms	remaining: 216ms
410:	learn: 0.0944914	total: 985ms	remaining: 213ms
411:	learn: 0.0940927	total: 988ms	remaining: 211ms
412:	learn: 0.0938162	total: 990ms	remaining: 209ms
413:	learn: 0.0934838	total: 992ms	remaining: 206ms
414:	learn: 0.0931976	total: 995ms	remaining: 204ms
415:	learn: 0.0927028	total: 997ms	remaining: 201ms
416:	learn: 0.0923305	total: 1000ms	remaining: 199ms
417:	learn: 0.0920084	total: 1s	remaining: 197ms
418:	learn: 0.0917376	total: 1s	remaining: 194ms
419:	learn: 0.0915133	total: 1.01s	remaining: 192ms
420:	learn: 0.0913757	total: 1.01s	remaining: 189ms
421:	learn: 0.0911044	total: 1.01s	remaining: 187ms
422:	learn: 0.0908398	total: 1.01s	remaining: 185ms
423:	learn: 0.0906052	total: 1.01s	remaining: 182ms
424:	learn: 0.0903910	total: 1.02s	remaining: 180ms
425:	learn: 0.0900635	total: 1.02s	remaining: 177ms
426:	learn: 0.0896944	total: 1.02s	remaining: 175ms
427:	learn: 0.0893653	total: 1.02s	remaining: 172ms
428:	learn: 0.0890843	total: 1.03s	remaining: 170ms
429:	learn: 0.0888224	total: 1.03s	remaining: 168ms
430:	learn: 0.0885144	total: 1.03s	remaining: 165ms
431:	learn: 0.0882682	total: 1.03s	remaining: 163ms
432:	learn: 0.0880202	total: 1.04s	remaining: 160ms
433:	learn: 0.0877350	total: 1.04s	remaining: 158ms
434:	learn: 0.0874350	total: 1.04s	remaining: 156ms
435:	learn: 0.0871092	total: 1.04s	remaining: 153ms
436:	learn: 0.0868829	total: 1.04s	remaining: 151ms
437:	learn: 0.0866632	total: 1.05s	remaining: 148ms
438:	learn: 0.0863579	total: 1.05s	remaining: 146ms
439:	learn: 0.0860636	total: 1.05s	remaining: 144ms
440:	learn: 0.0857363	total: 1.06s	remaining: 141ms
441:	learn: 0.0854836	total: 1.06s	remaining: 139ms
442:	learn: 0.0852161	total: 1.06s	remaining: 137ms
443:	learn: 0.0849539	total: 1.06s	remaining: 134ms
444:	learn: 0.0846941	total: 1.07s	remaining: 132ms
445:	learn: 0.0844522	total: 1.07s	remaining: 129ms
446:	learn: 0.0841935	total: 1.07s	remaining: 127ms
447:	learn: 0.0839719	total: 1.07s	remaining: 125ms
448:	learn: 0.0836746	total: 1.07s	remaining: 122ms
449:	learn: 0.0833860	total: 1.08s	remaining: 120ms
450:	learn: 0.0831446	total: 1.08s	remaining: 117ms
451:	learn: 0.0828217	total: 1.08s	remaining: 115ms
452:	learn: 0.0824639	total: 1.08s	remaining: 113ms
453:	learn: 0.0822226	total: 1.09s	remaining: 110ms
454:	learn: 0.0819512	total: 1.09s	remaining: 108ms
455:	learn: 0.0816107	total: 1.09s	remaining: 105ms
456:	learn: 0.0814032	total: 1.09s	remaining: 103ms
457:	learn: 0.0811297	total: 1.09s	remaining: 100ms
458:	learn: 0.0807890	total: 1.1s	remaining: 98.1ms
459:	learn: 0.0805082	total: 1.1s	remaining: 95.7ms
460:	learn: 0.0802133	total: 1.1s	remaining: 93.3ms
461:	learn: 0.0799095	total: 1.1s	remaining: 90.9ms
462:	learn: 0.0795839	total: 1.11s	remaining: 88.5ms
463:	learn: 0.0793099	total: 1.11s	remaining: 86.1ms
464:	learn: 0.0790846	total: 1.11s	remaining: 83.7ms
465:	learn: 0.0787365	total: 1.11s	remaining: 81.3ms
466:	learn: 0.0784465	total: 1.12s	remaining: 78.9ms
467:	learn: 0.0781954	total: 1.12s	remaining: 76.5ms
468:	learn: 0.0779743	total: 1.12s	remaining: 74.1ms
469:	learn: 0.0776451	total: 1.12s	remaining: 71.7ms
470:	learn: 0.0774328	total: 1.13s	remaining: 69.3ms
471:	learn: 0.0770602	total: 1.13s	remaining: 66.9ms
472:	learn: 0.0767977	total: 1.13s	remaining: 64.5ms
473:	learn: 0.0765830	total: 1.13s	remaining: 62.1ms
474:	learn: 0.0762757	total: 1.13s	remaining: 59.7ms
475:	learn: 0.0760997	total: 1.14s	remaining: 57.3ms
476:	learn: 0.0758055	total: 1.14s	remaining: 54.9ms
477:	learn: 0.0756213	total: 1.14s	remaining: 52.5ms
478:	learn: 0.0753523	total: 1.14s	remaining: 50.1ms
479:	learn: 0.0751104	total: 1.15s	remaining: 47.7ms
480:	learn: 0.0749080	total: 1.15s	remaining: 45.3ms
481:	learn: 0.0745176	total: 1.15s	remaining: 43.1ms
482:	learn: 0.0742888	total: 1.16s	remaining: 40.7ms
483:	learn: 0.0741204	total: 1.16s	remaining: 38.3ms
484:	learn: 0.0738983	total: 1.16s	remaining: 35.9ms
485:	learn: 0.0737025	total: 1.16s	remaining: 33.5ms
486:	learn: 0.0734902	total: 1.16s	remaining: 31.1ms
487:	learn: 0.0733288	total: 1.17s	remaining: 28.7ms
488:	learn: 0.0731091	total: 1.17s	remaining: 26.3ms
489:	learn: 0.0728463	total: 1.17s	remaining: 23.9ms
490:	learn: 0.0725469	total: 1.17s	remaining: 21.5ms
491:	learn: 0.0723841	total: 1.18s	remaining: 19.1ms
492:	learn: 0.0721742	total: 1.18s	remaining: 16.7ms
493:	learn: 0.0719615	total: 1.18s	remaining: 14.3ms
494:	learn: 0.0717685	total: 1.18s	remaining: 11.9ms
495:	learn: 0.0715817	total: 1.18s	remaining: 9.55ms
496:	learn: 0.0712956	total: 1.19s	remaining: 7.17ms
497:	learn: 0.0710543	total: 1.19s	remaining: 4.78ms
498:	learn: 0.0708175	total: 1.19s	remaining: 2.39ms
499:	learn: 0.0705544	total: 1.19s	remaining: 0us
0:	learn: 0.6876145	total: 2.94ms	remaining: 1.46s
1:	learn: 0.6813307	total: 5.71ms	remaining: 1.42s
2:	learn: 0.6769142	total: 7.95ms	remaining: 1.32s
3:	learn: 0.6708767	total: 10.2ms	remaining: 1.26s
4:	learn: 0.6661109	total: 12.5ms	remaining: 1.24s
5:	learn: 0.6588592	total: 14.8ms	remaining: 1.22s
6:	learn: 0.6528363	total: 17ms	remaining: 1.2s
7:	learn: 0.6478514	total: 19.3ms	remaining: 1.18s
8:	learn: 0.6429200	total: 21.5ms	remaining: 1.17s
9:	learn: 0.6376456	total: 23.9ms	remaining: 1.17s
10:	learn: 0.6316737	total: 26.2ms	remaining: 1.17s
11:	learn: 0.6274481	total: 28.5ms	remaining: 1.16s
12:	learn: 0.6233334	total: 30.7ms	remaining: 1.15s
13:	learn: 0.6178126	total: 33ms	remaining: 1.15s
14:	learn: 0.6130511	total: 35.3ms	remaining: 1.14s
15:	learn: 0.6100610	total: 37.5ms	remaining: 1.13s
16:	learn: 0.6051445	total: 39.8ms	remaining: 1.13s
17:	learn: 0.6005914	total: 42.1ms	remaining: 1.13s
18:	learn: 0.5959372	total: 44.6ms	remaining: 1.13s
19:	learn: 0.5919222	total: 46.8ms	remaining: 1.12s
20:	learn: 0.5874813	total: 49.1ms	remaining: 1.12s
21:	learn: 0.5824266	total: 51.4ms	remaining: 1.12s
22:	learn: 0.5784595	total: 53.7ms	remaining: 1.11s
23:	learn: 0.5744303	total: 55.9ms	remaining: 1.11s
24:	learn: 0.5691179	total: 58.6ms	remaining: 1.11s
25:	learn: 0.5643364	total: 61.8ms	remaining: 1.13s
26:	learn: 0.5601804	total: 64ms	remaining: 1.12s
27:	learn: 0.5553696	total: 66.3ms	remaining: 1.12s
28:	learn: 0.5517236	total: 68.7ms	remaining: 1.11s
29:	learn: 0.5484153	total: 71ms	remaining: 1.11s
30:	learn: 0.5442821	total: 73.3ms	remaining: 1.11s
31:	learn: 0.5412631	total: 75.7ms	remaining: 1.11s
32:	learn: 0.5365921	total: 77.8ms	remaining: 1.1s
33:	learn: 0.5327339	total: 80.1ms	remaining: 1.1s
34:	learn: 0.5286806	total: 82.5ms	remaining: 1.09s
35:	learn: 0.5244020	total: 84.8ms	remaining: 1.09s
36:	learn: 0.5209295	total: 87.2ms	remaining: 1.09s
37:	learn: 0.5164516	total: 89.6ms	remaining: 1.09s
38:	learn: 0.5120724	total: 91.9ms	remaining: 1.09s
39:	learn: 0.5078363	total: 94.1ms	remaining: 1.08s
40:	learn: 0.5040076	total: 96.3ms	remaining: 1.08s
41:	learn: 0.5007129	total: 98.6ms	remaining: 1.07s
42:	learn: 0.4979734	total: 101ms	remaining: 1.08s
43:	learn: 0.4941783	total: 104ms	remaining: 1.07s
44:	learn: 0.4912941	total: 106ms	remaining: 1.07s
45:	learn: 0.4881257	total: 108ms	remaining: 1.07s
46:	learn: 0.4843744	total: 110ms	remaining: 1.06s
47:	learn: 0.4809667	total: 113ms	remaining: 1.06s
48:	learn: 0.4780726	total: 115ms	remaining: 1.06s
49:	learn: 0.4748775	total: 117ms	remaining: 1.05s
50:	learn: 0.4717820	total: 119ms	remaining: 1.05s
51:	learn: 0.4682980	total: 122ms	remaining: 1.05s
52:	learn: 0.4656011	total: 124ms	remaining: 1.04s
53:	learn: 0.4630222	total: 126ms	remaining: 1.04s
54:	learn: 0.4591813	total: 128ms	remaining: 1.04s
55:	learn: 0.4561935	total: 131ms	remaining: 1.04s
56:	learn: 0.4528537	total: 133ms	remaining: 1.03s
57:	learn: 0.4496711	total: 135ms	remaining: 1.03s
58:	learn: 0.4469360	total: 138ms	remaining: 1.03s
59:	learn: 0.4436038	total: 140ms	remaining: 1.03s
60:	learn: 0.4408056	total: 142ms	remaining: 1.02s
61:	learn: 0.4376064	total: 144ms	remaining: 1.02s
62:	learn: 0.4348059	total: 147ms	remaining: 1.02s
63:	learn: 0.4315747	total: 149ms	remaining: 1.01s
64:	learn: 0.4286842	total: 151ms	remaining: 1.01s
65:	learn: 0.4256618	total: 154ms	remaining: 1.01s
66:	learn: 0.4219698	total: 156ms	remaining: 1.01s
67:	learn: 0.4195004	total: 158ms	remaining: 1s
68:	learn: 0.4171539	total: 161ms	remaining: 1s
69:	learn: 0.4146791	total: 163ms	remaining: 1s
70:	learn: 0.4113268	total: 165ms	remaining: 999ms
71:	learn: 0.4091552	total: 168ms	remaining: 996ms
72:	learn: 0.4068781	total: 170ms	remaining: 994ms
73:	learn: 0.4048618	total: 172ms	remaining: 992ms
74:	learn: 0.4020219	total: 174ms	remaining: 988ms
75:	learn: 0.3985429	total: 177ms	remaining: 985ms
76:	learn: 0.3963602	total: 179ms	remaining: 983ms
77:	learn: 0.3934867	total: 181ms	remaining: 981ms
78:	learn: 0.3907507	total: 184ms	remaining: 979ms
79:	learn: 0.3885513	total: 186ms	remaining: 976ms
80:	learn: 0.3861112	total: 188ms	remaining: 974ms
81:	learn: 0.3843472	total: 190ms	remaining: 971ms
82:	learn: 0.3820671	total: 193ms	remaining: 968ms
83:	learn: 0.3793829	total: 195ms	remaining: 966ms
84:	learn: 0.3770914	total: 198ms	remaining: 964ms
85:	learn: 0.3747327	total: 200ms	remaining: 963ms
86:	learn: 0.3730284	total: 202ms	remaining: 960ms
87:	learn: 0.3708352	total: 205ms	remaining: 958ms
88:	learn: 0.3687752	total: 207ms	remaining: 956ms
89:	learn: 0.3666878	total: 209ms	remaining: 953ms
90:	learn: 0.3650104	total: 211ms	remaining: 950ms
91:	learn: 0.3620211	total: 214ms	remaining: 947ms
92:	learn: 0.3597162	total: 216ms	remaining: 945ms
93:	learn: 0.3579976	total: 218ms	remaining: 942ms
94:	learn: 0.3564529	total: 220ms	remaining: 940ms
95:	learn: 0.3547447	total: 223ms	remaining: 937ms
96:	learn: 0.3526845	total: 225ms	remaining: 935ms
97:	learn: 0.3506872	total: 227ms	remaining: 933ms
98:	learn: 0.3485371	total: 230ms	remaining: 930ms
99:	learn: 0.3461850	total: 232ms	remaining: 928ms
100:	learn: 0.3442065	total: 234ms	remaining: 925ms
101:	learn: 0.3426900	total: 236ms	remaining: 923ms
102:	learn: 0.3408338	total: 239ms	remaining: 921ms
103:	learn: 0.3390559	total: 241ms	remaining: 918ms
104:	learn: 0.3370488	total: 243ms	remaining: 915ms
105:	learn: 0.3351551	total: 246ms	remaining: 913ms
106:	learn: 0.3328687	total: 248ms	remaining: 910ms
107:	learn: 0.3312149	total: 250ms	remaining: 908ms
108:	learn: 0.3288516	total: 252ms	remaining: 905ms
109:	learn: 0.3272244	total: 255ms	remaining: 903ms
110:	learn: 0.3251416	total: 257ms	remaining: 900ms
111:	learn: 0.3229643	total: 259ms	remaining: 898ms
112:	learn: 0.3209719	total: 261ms	remaining: 895ms
113:	learn: 0.3189454	total: 264ms	remaining: 892ms
114:	learn: 0.3172607	total: 266ms	remaining: 890ms
115:	learn: 0.3156412	total: 268ms	remaining: 888ms
116:	learn: 0.3141753	total: 270ms	remaining: 885ms
117:	learn: 0.3125642	total: 273ms	remaining: 883ms
118:	learn: 0.3119274	total: 275ms	remaining: 880ms
119:	learn: 0.3100246	total: 277ms	remaining: 878ms
120:	learn: 0.3081458	total: 279ms	remaining: 875ms
121:	learn: 0.3062325	total: 282ms	remaining: 872ms
122:	learn: 0.3047193	total: 284ms	remaining: 870ms
123:	learn: 0.3035533	total: 287ms	remaining: 869ms
124:	learn: 0.3016130	total: 289ms	remaining: 867ms
125:	learn: 0.3000435	total: 291ms	remaining: 864ms
126:	learn: 0.2983039	total: 294ms	remaining: 863ms
127:	learn: 0.2970017	total: 296ms	remaining: 860ms
128:	learn: 0.2955011	total: 298ms	remaining: 858ms
129:	learn: 0.2941442	total: 300ms	remaining: 855ms
130:	learn: 0.2926751	total: 303ms	remaining: 853ms
131:	learn: 0.2912907	total: 305ms	remaining: 851ms
132:	learn: 0.2894913	total: 307ms	remaining: 848ms
133:	learn: 0.2878024	total: 310ms	remaining: 846ms
134:	learn: 0.2861371	total: 312ms	remaining: 843ms
135:	learn: 0.2846388	total: 314ms	remaining: 841ms
136:	learn: 0.2833521	total: 316ms	remaining: 838ms
137:	learn: 0.2819626	total: 319ms	remaining: 836ms
138:	learn: 0.2804224	total: 321ms	remaining: 834ms
139:	learn: 0.2786126	total: 323ms	remaining: 831ms
140:	learn: 0.2778698	total: 326ms	remaining: 829ms
141:	learn: 0.2766950	total: 328ms	remaining: 827ms
142:	learn: 0.2755001	total: 330ms	remaining: 824ms
143:	learn: 0.2743569	total: 332ms	remaining: 822ms
144:	learn: 0.2726324	total: 335ms	remaining: 819ms
145:	learn: 0.2712792	total: 337ms	remaining: 817ms
146:	learn: 0.2701971	total: 339ms	remaining: 815ms
147:	learn: 0.2687132	total: 342ms	remaining: 813ms
148:	learn: 0.2672829	total: 345ms	remaining: 812ms
149:	learn: 0.2658099	total: 348ms	remaining: 812ms
150:	learn: 0.2639742	total: 351ms	remaining: 810ms
151:	learn: 0.2625032	total: 353ms	remaining: 809ms
152:	learn: 0.2609381	total: 356ms	remaining: 807ms
153:	learn: 0.2597085	total: 358ms	remaining: 805ms
154:	learn: 0.2579355	total: 361ms	remaining: 803ms
155:	learn: 0.2571596	total: 363ms	remaining: 801ms
156:	learn: 0.2561994	total: 366ms	remaining: 799ms
157:	learn: 0.2551461	total: 368ms	remaining: 797ms
158:	learn: 0.2542675	total: 371ms	remaining: 795ms
159:	learn: 0.2534017	total: 373ms	remaining: 793ms
160:	learn: 0.2523988	total: 376ms	remaining: 791ms
161:	learn: 0.2512010	total: 378ms	remaining: 789ms
162:	learn: 0.2494965	total: 380ms	remaining: 786ms
163:	learn: 0.2484071	total: 383ms	remaining: 784ms
164:	learn: 0.2469091	total: 385ms	remaining: 782ms
165:	learn: 0.2459740	total: 388ms	remaining: 780ms
166:	learn: 0.2444739	total: 390ms	remaining: 778ms
167:	learn: 0.2430587	total: 393ms	remaining: 776ms
168:	learn: 0.2418342	total: 396ms	remaining: 775ms
169:	learn: 0.2405280	total: 398ms	remaining: 773ms
170:	learn: 0.2391440	total: 401ms	remaining: 771ms
171:	learn: 0.2381130	total: 403ms	remaining: 768ms
172:	learn: 0.2372050	total: 405ms	remaining: 766ms
173:	learn: 0.2356934	total: 408ms	remaining: 764ms
174:	learn: 0.2345065	total: 410ms	remaining: 762ms
175:	learn: 0.2338636	total: 413ms	remaining: 760ms
176:	learn: 0.2328151	total: 415ms	remaining: 758ms
177:	learn: 0.2317163	total: 418ms	remaining: 756ms
178:	learn: 0.2307648	total: 420ms	remaining: 753ms
179:	learn: 0.2297866	total: 423ms	remaining: 751ms
180:	learn: 0.2284517	total: 425ms	remaining: 749ms
181:	learn: 0.2271911	total: 428ms	remaining: 747ms
182:	learn: 0.2265378	total: 430ms	remaining: 745ms
183:	learn: 0.2257832	total: 432ms	remaining: 742ms
184:	learn: 0.2250393	total: 434ms	remaining: 740ms
185:	learn: 0.2242620	total: 437ms	remaining: 737ms
186:	learn: 0.2229313	total: 439ms	remaining: 735ms
187:	learn: 0.2216211	total: 441ms	remaining: 732ms
188:	learn: 0.2203095	total: 443ms	remaining: 730ms
189:	learn: 0.2195388	total: 446ms	remaining: 727ms
190:	learn: 0.2187262	total: 448ms	remaining: 725ms
191:	learn: 0.2176294	total: 450ms	remaining: 722ms
192:	learn: 0.2165392	total: 452ms	remaining: 720ms
193:	learn: 0.2156308	total: 455ms	remaining: 717ms
194:	learn: 0.2147778	total: 457ms	remaining: 714ms
195:	learn: 0.2138893	total: 459ms	remaining: 712ms
196:	learn: 0.2132339	total: 461ms	remaining: 710ms
197:	learn: 0.2123567	total: 464ms	remaining: 707ms
198:	learn: 0.2114900	total: 466ms	remaining: 704ms
199:	learn: 0.2104609	total: 468ms	remaining: 702ms
200:	learn: 0.2096746	total: 471ms	remaining: 700ms
201:	learn: 0.2087586	total: 473ms	remaining: 698ms
202:	learn: 0.2079130	total: 476ms	remaining: 696ms
203:	learn: 0.2071526	total: 478ms	remaining: 694ms
204:	learn: 0.2060478	total: 481ms	remaining: 691ms
205:	learn: 0.2048520	total: 483ms	remaining: 689ms
206:	learn: 0.2039855	total: 485ms	remaining: 687ms
207:	learn: 0.2030069	total: 489ms	remaining: 686ms
208:	learn: 0.2022732	total: 491ms	remaining: 684ms
209:	learn: 0.2013827	total: 494ms	remaining: 682ms
210:	learn: 0.2006028	total: 496ms	remaining: 680ms
211:	learn: 0.1998979	total: 499ms	remaining: 677ms
212:	learn: 0.1988519	total: 501ms	remaining: 675ms
213:	learn: 0.1978849	total: 504ms	remaining: 673ms
214:	learn: 0.1968665	total: 506ms	remaining: 671ms
215:	learn: 0.1960271	total: 508ms	remaining: 668ms
216:	learn: 0.1955368	total: 511ms	remaining: 666ms
217:	learn: 0.1946451	total: 513ms	remaining: 664ms
218:	learn: 0.1934560	total: 516ms	remaining: 662ms
219:	learn: 0.1926662	total: 518ms	remaining: 659ms
220:	learn: 0.1919880	total: 521ms	remaining: 657ms
221:	learn: 0.1911067	total: 523ms	remaining: 655ms
222:	learn: 0.1902587	total: 526ms	remaining: 653ms
223:	learn: 0.1896159	total: 528ms	remaining: 651ms
224:	learn: 0.1883044	total: 531ms	remaining: 648ms
225:	learn: 0.1873535	total: 533ms	remaining: 646ms
226:	learn: 0.1862554	total: 535ms	remaining: 644ms
227:	learn: 0.1854611	total: 538ms	remaining: 641ms
228:	learn: 0.1847938	total: 540ms	remaining: 639ms
229:	learn: 0.1839586	total: 543ms	remaining: 637ms
230:	learn: 0.1832771	total: 545ms	remaining: 635ms
231:	learn: 0.1824126	total: 547ms	remaining: 632ms
232:	learn: 0.1816569	total: 550ms	remaining: 630ms
233:	learn: 0.1811585	total: 552ms	remaining: 628ms
234:	learn: 0.1802681	total: 555ms	remaining: 626ms
235:	learn: 0.1795564	total: 557ms	remaining: 623ms
236:	learn: 0.1787962	total: 560ms	remaining: 621ms
237:	learn: 0.1780100	total: 562ms	remaining: 618ms
238:	learn: 0.1773462	total: 564ms	remaining: 616ms
239:	learn: 0.1767461	total: 566ms	remaining: 614ms
240:	learn: 0.1760052	total: 569ms	remaining: 611ms
241:	learn: 0.1752357	total: 571ms	remaining: 609ms
242:	learn: 0.1747062	total: 573ms	remaining: 606ms
243:	learn: 0.1740347	total: 575ms	remaining: 604ms
244:	learn: 0.1732299	total: 578ms	remaining: 601ms
245:	learn: 0.1723306	total: 580ms	remaining: 599ms
246:	learn: 0.1716685	total: 582ms	remaining: 596ms
247:	learn: 0.1709364	total: 585ms	remaining: 594ms
248:	learn: 0.1704605	total: 587ms	remaining: 592ms
249:	learn: 0.1696595	total: 590ms	remaining: 590ms
250:	learn: 0.1690776	total: 592ms	remaining: 587ms
251:	learn: 0.1686728	total: 594ms	remaining: 585ms
252:	learn: 0.1680044	total: 597ms	remaining: 583ms
253:	learn: 0.1672626	total: 599ms	remaining: 580ms
254:	learn: 0.1668057	total: 601ms	remaining: 578ms
255:	learn: 0.1661801	total: 603ms	remaining: 575ms
256:	learn: 0.1654204	total: 606ms	remaining: 573ms
257:	learn: 0.1648168	total: 608ms	remaining: 570ms
258:	learn: 0.1639118	total: 611ms	remaining: 568ms
259:	learn: 0.1633690	total: 613ms	remaining: 566ms
260:	learn: 0.1628656	total: 615ms	remaining: 563ms
261:	learn: 0.1622523	total: 618ms	remaining: 561ms
262:	learn: 0.1616780	total: 620ms	remaining: 558ms
263:	learn: 0.1611871	total: 622ms	remaining: 556ms
264:	learn: 0.1604660	total: 624ms	remaining: 554ms
265:	learn: 0.1598941	total: 626ms	remaining: 551ms
266:	learn: 0.1593164	total: 629ms	remaining: 549ms
267:	learn: 0.1586906	total: 631ms	remaining: 546ms
268:	learn: 0.1580322	total: 633ms	remaining: 544ms
269:	learn: 0.1572847	total: 636ms	remaining: 541ms
270:	learn: 0.1565977	total: 638ms	remaining: 539ms
271:	learn: 0.1559524	total: 640ms	remaining: 537ms
272:	learn: 0.1553193	total: 643ms	remaining: 534ms
273:	learn: 0.1546547	total: 645ms	remaining: 532ms
274:	learn: 0.1541516	total: 647ms	remaining: 529ms
275:	learn: 0.1536788	total: 649ms	remaining: 527ms
276:	learn: 0.1531843	total: 652ms	remaining: 525ms
277:	learn: 0.1524104	total: 654ms	remaining: 522ms
278:	learn: 0.1516021	total: 656ms	remaining: 520ms
279:	learn: 0.1511304	total: 659ms	remaining: 517ms
280:	learn: 0.1504880	total: 661ms	remaining: 515ms
281:	learn: 0.1498146	total: 663ms	remaining: 513ms
282:	learn: 0.1493242	total: 665ms	remaining: 510ms
283:	learn: 0.1488062	total: 668ms	remaining: 508ms
284:	learn: 0.1482570	total: 670ms	remaining: 505ms
285:	learn: 0.1476605	total: 672ms	remaining: 503ms
286:	learn: 0.1471862	total: 674ms	remaining: 501ms
287:	learn: 0.1465218	total: 677ms	remaining: 498ms
288:	learn: 0.1460307	total: 679ms	remaining: 496ms
289:	learn: 0.1453883	total: 682ms	remaining: 494ms
290:	learn: 0.1448839	total: 685ms	remaining: 492ms
291:	learn: 0.1442658	total: 688ms	remaining: 490ms
292:	learn: 0.1439111	total: 690ms	remaining: 488ms
293:	learn: 0.1434085	total: 693ms	remaining: 485ms
294:	learn: 0.1428991	total: 695ms	remaining: 483ms
295:	learn: 0.1424335	total: 697ms	remaining: 481ms
296:	learn: 0.1418606	total: 700ms	remaining: 478ms
297:	learn: 0.1415439	total: 702ms	remaining: 476ms
298:	learn: 0.1410552	total: 704ms	remaining: 474ms
299:	learn: 0.1405348	total: 707ms	remaining: 471ms
300:	learn: 0.1401208	total: 709ms	remaining: 469ms
301:	learn: 0.1395721	total: 711ms	remaining: 466ms
302:	learn: 0.1388538	total: 714ms	remaining: 464ms
303:	learn: 0.1383506	total: 716ms	remaining: 462ms
304:	learn: 0.1379272	total: 718ms	remaining: 459ms
305:	learn: 0.1373773	total: 721ms	remaining: 457ms
306:	learn: 0.1369874	total: 723ms	remaining: 454ms
307:	learn: 0.1364314	total: 725ms	remaining: 452ms
308:	learn: 0.1360116	total: 727ms	remaining: 450ms
309:	learn: 0.1354737	total: 730ms	remaining: 447ms
310:	learn: 0.1349160	total: 732ms	remaining: 445ms
311:	learn: 0.1343934	total: 734ms	remaining: 442ms
312:	learn: 0.1338373	total: 736ms	remaining: 440ms
313:	learn: 0.1333497	total: 739ms	remaining: 438ms
314:	learn: 0.1328659	total: 741ms	remaining: 435ms
315:	learn: 0.1322859	total: 743ms	remaining: 433ms
316:	learn: 0.1318274	total: 745ms	remaining: 430ms
317:	learn: 0.1311571	total: 748ms	remaining: 428ms
318:	learn: 0.1305477	total: 750ms	remaining: 426ms
319:	learn: 0.1299857	total: 752ms	remaining: 423ms
320:	learn: 0.1295459	total: 755ms	remaining: 421ms
321:	learn: 0.1290764	total: 757ms	remaining: 418ms
322:	learn: 0.1284017	total: 759ms	remaining: 416ms
323:	learn: 0.1280080	total: 761ms	remaining: 414ms
324:	learn: 0.1275723	total: 763ms	remaining: 411ms
325:	learn: 0.1272327	total: 766ms	remaining: 409ms
326:	learn: 0.1267823	total: 768ms	remaining: 406ms
327:	learn: 0.1263886	total: 770ms	remaining: 404ms
328:	learn: 0.1259754	total: 773ms	remaining: 402ms
329:	learn: 0.1256255	total: 775ms	remaining: 399ms
330:	learn: 0.1251350	total: 779ms	remaining: 398ms
331:	learn: 0.1247164	total: 781ms	remaining: 395ms
332:	learn: 0.1242707	total: 784ms	remaining: 393ms
333:	learn: 0.1239089	total: 786ms	remaining: 391ms
334:	learn: 0.1234834	total: 789ms	remaining: 388ms
335:	learn: 0.1228953	total: 791ms	remaining: 386ms
336:	learn: 0.1222711	total: 793ms	remaining: 384ms
337:	learn: 0.1218469	total: 795ms	remaining: 381ms
338:	learn: 0.1213772	total: 798ms	remaining: 379ms
339:	learn: 0.1209542	total: 800ms	remaining: 376ms
340:	learn: 0.1205926	total: 802ms	remaining: 374ms
341:	learn: 0.1202656	total: 804ms	remaining: 372ms
342:	learn: 0.1198579	total: 807ms	remaining: 369ms
343:	learn: 0.1194590	total: 809ms	remaining: 367ms
344:	learn: 0.1190246	total: 811ms	remaining: 364ms
345:	learn: 0.1184797	total: 814ms	remaining: 362ms
346:	learn: 0.1180301	total: 816ms	remaining: 360ms
347:	learn: 0.1175776	total: 818ms	remaining: 357ms
348:	learn: 0.1170493	total: 820ms	remaining: 355ms
349:	learn: 0.1166533	total: 823ms	remaining: 353ms
350:	learn: 0.1163717	total: 825ms	remaining: 350ms
351:	learn: 0.1160476	total: 827ms	remaining: 348ms
352:	learn: 0.1156427	total: 829ms	remaining: 345ms
353:	learn: 0.1151768	total: 832ms	remaining: 343ms
354:	learn: 0.1147622	total: 834ms	remaining: 341ms
355:	learn: 0.1143942	total: 836ms	remaining: 338ms
356:	learn: 0.1139147	total: 838ms	remaining: 336ms
357:	learn: 0.1136117	total: 841ms	remaining: 333ms
358:	learn: 0.1132063	total: 843ms	remaining: 331ms
359:	learn: 0.1127241	total: 845ms	remaining: 329ms
360:	learn: 0.1124094	total: 847ms	remaining: 326ms
361:	learn: 0.1122060	total: 849ms	remaining: 324ms
362:	learn: 0.1119059	total: 852ms	remaining: 321ms
363:	learn: 0.1115916	total: 854ms	remaining: 319ms
364:	learn: 0.1112617	total: 856ms	remaining: 317ms
365:	learn: 0.1109305	total: 858ms	remaining: 314ms
366:	learn: 0.1106680	total: 861ms	remaining: 312ms
367:	learn: 0.1103090	total: 863ms	remaining: 309ms
368:	learn: 0.1098920	total: 865ms	remaining: 307ms
369:	learn: 0.1095713	total: 867ms	remaining: 305ms
370:	learn: 0.1093282	total: 869ms	remaining: 302ms
371:	learn: 0.1090372	total: 872ms	remaining: 300ms
372:	learn: 0.1086270	total: 876ms	remaining: 298ms
373:	learn: 0.1081172	total: 878ms	remaining: 296ms
374:	learn: 0.1077446	total: 880ms	remaining: 293ms
375:	learn: 0.1072801	total: 883ms	remaining: 291ms
376:	learn: 0.1068429	total: 885ms	remaining: 289ms
377:	learn: 0.1064504	total: 887ms	remaining: 286ms
378:	learn: 0.1060781	total: 890ms	remaining: 284ms
379:	learn: 0.1055864	total: 892ms	remaining: 282ms
380:	learn: 0.1051483	total: 894ms	remaining: 279ms
381:	learn: 0.1049294	total: 897ms	remaining: 277ms
382:	learn: 0.1045448	total: 899ms	remaining: 275ms
383:	learn: 0.1041867	total: 901ms	remaining: 272ms
384:	learn: 0.1038676	total: 903ms	remaining: 270ms
385:	learn: 0.1034361	total: 906ms	remaining: 267ms
386:	learn: 0.1029515	total: 908ms	remaining: 265ms
387:	learn: 0.1025575	total: 910ms	remaining: 263ms
388:	learn: 0.1023655	total: 912ms	remaining: 260ms
389:	learn: 0.1019865	total: 915ms	remaining: 258ms
390:	learn: 0.1016078	total: 917ms	remaining: 256ms
391:	learn: 0.1011274	total: 919ms	remaining: 253ms
392:	learn: 0.1006944	total: 921ms	remaining: 251ms
393:	learn: 0.1003566	total: 923ms	remaining: 248ms
394:	learn: 0.1000366	total: 926ms	remaining: 246ms
395:	learn: 0.0996251	total: 928ms	remaining: 244ms
396:	learn: 0.0991812	total: 930ms	remaining: 241ms
397:	learn: 0.0988458	total: 933ms	remaining: 239ms
398:	learn: 0.0984846	total: 935ms	remaining: 237ms
399:	learn: 0.0981340	total: 937ms	remaining: 234ms
400:	learn: 0.0977330	total: 939ms	remaining: 232ms
401:	learn: 0.0974708	total: 942ms	remaining: 230ms
402:	learn: 0.0971976	total: 944ms	remaining: 227ms
403:	learn: 0.0969141	total: 946ms	remaining: 225ms
404:	learn: 0.0966426	total: 948ms	remaining: 222ms
405:	learn: 0.0963080	total: 951ms	remaining: 220ms
406:	learn: 0.0959973	total: 953ms	remaining: 218ms
407:	learn: 0.0957456	total: 955ms	remaining: 215ms
408:	learn: 0.0954161	total: 957ms	remaining: 213ms
409:	learn: 0.0951757	total: 960ms	remaining: 211ms
410:	learn: 0.0948696	total: 962ms	remaining: 208ms
411:	learn: 0.0945712	total: 964ms	remaining: 206ms
412:	learn: 0.0942194	total: 966ms	remaining: 204ms
413:	learn: 0.0939048	total: 969ms	remaining: 201ms
414:	learn: 0.0935423	total: 971ms	remaining: 199ms
415:	learn: 0.0932516	total: 974ms	remaining: 197ms
416:	learn: 0.0929225	total: 976ms	remaining: 194ms
417:	learn: 0.0926690	total: 978ms	remaining: 192ms
418:	learn: 0.0923440	total: 981ms	remaining: 190ms
419:	learn: 0.0919881	total: 983ms	remaining: 187ms
420:	learn: 0.0918629	total: 985ms	remaining: 185ms
421:	learn: 0.0916174	total: 988ms	remaining: 183ms
422:	learn: 0.0913244	total: 990ms	remaining: 180ms
423:	learn: 0.0910479	total: 992ms	remaining: 178ms
424:	learn: 0.0907494	total: 994ms	remaining: 175ms
425:	learn: 0.0904944	total: 997ms	remaining: 173ms
426:	learn: 0.0900787	total: 999ms	remaining: 171ms
427:	learn: 0.0897154	total: 1s	remaining: 168ms
428:	learn: 0.0894008	total: 1s	remaining: 166ms
429:	learn: 0.0889810	total: 1s	remaining: 164ms
430:	learn: 0.0887333	total: 1.01s	remaining: 161ms
431:	learn: 0.0884548	total: 1.01s	remaining: 159ms
432:	learn: 0.0882232	total: 1.01s	remaining: 157ms
433:	learn: 0.0879324	total: 1.01s	remaining: 154ms
434:	learn: 0.0877179	total: 1.02s	remaining: 152ms
435:	learn: 0.0874198	total: 1.02s	remaining: 150ms
436:	learn: 0.0871514	total: 1.02s	remaining: 147ms
437:	learn: 0.0868366	total: 1.02s	remaining: 145ms
438:	learn: 0.0866194	total: 1.02s	remaining: 143ms
439:	learn: 0.0864005	total: 1.03s	remaining: 140ms
440:	learn: 0.0862125	total: 1.03s	remaining: 138ms
441:	learn: 0.0860386	total: 1.03s	remaining: 135ms
442:	learn: 0.0857008	total: 1.03s	remaining: 133ms
443:	learn: 0.0853829	total: 1.04s	remaining: 131ms
444:	learn: 0.0851059	total: 1.04s	remaining: 128ms
445:	learn: 0.0848627	total: 1.04s	remaining: 126ms
446:	learn: 0.0845904	total: 1.04s	remaining: 124ms
447:	learn: 0.0843207	total: 1.05s	remaining: 121ms
448:	learn: 0.0839758	total: 1.05s	remaining: 119ms
449:	learn: 0.0837027	total: 1.05s	remaining: 117ms
450:	learn: 0.0833723	total: 1.05s	remaining: 114ms
451:	learn: 0.0831047	total: 1.05s	remaining: 112ms
452:	learn: 0.0828712	total: 1.06s	remaining: 110ms
453:	learn: 0.0825631	total: 1.06s	remaining: 107ms
454:	learn: 0.0822714	total: 1.06s	remaining: 105ms
455:	learn: 0.0820221	total: 1.06s	remaining: 103ms
456:	learn: 0.0817773	total: 1.07s	remaining: 101ms
457:	learn: 0.0814604	total: 1.07s	remaining: 98.2ms
458:	learn: 0.0811643	total: 1.07s	remaining: 95.8ms
459:	learn: 0.0809024	total: 1.07s	remaining: 93.5ms
460:	learn: 0.0806277	total: 1.08s	remaining: 91.1ms
461:	learn: 0.0803408	total: 1.08s	remaining: 88.8ms
462:	learn: 0.0800468	total: 1.08s	remaining: 86.5ms
463:	learn: 0.0798103	total: 1.08s	remaining: 84.1ms
464:	learn: 0.0795678	total: 1.09s	remaining: 81.8ms
465:	learn: 0.0793008	total: 1.09s	remaining: 79.4ms
466:	learn: 0.0790273	total: 1.09s	remaining: 77.1ms
467:	learn: 0.0787133	total: 1.09s	remaining: 74.7ms
468:	learn: 0.0784725	total: 1.09s	remaining: 72.4ms
469:	learn: 0.0782154	total: 1.1s	remaining: 70.1ms
470:	learn: 0.0779618	total: 1.1s	remaining: 67.7ms
471:	learn: 0.0776610	total: 1.1s	remaining: 65.4ms
472:	learn: 0.0774703	total: 1.1s	remaining: 63ms
473:	learn: 0.0771911	total: 1.11s	remaining: 60.7ms
474:	learn: 0.0769537	total: 1.11s	remaining: 58.4ms
475:	learn: 0.0767690	total: 1.11s	remaining: 56ms
476:	learn: 0.0764864	total: 1.11s	remaining: 53.7ms
477:	learn: 0.0763032	total: 1.11s	remaining: 51.3ms
478:	learn: 0.0760323	total: 1.12s	remaining: 49ms
479:	learn: 0.0758105	total: 1.12s	remaining: 46.7ms
480:	learn: 0.0756198	total: 1.12s	remaining: 44.3ms
481:	learn: 0.0753579	total: 1.12s	remaining: 42ms
482:	learn: 0.0751503	total: 1.13s	remaining: 39.7ms
483:	learn: 0.0749224	total: 1.13s	remaining: 37.3ms
484:	learn: 0.0747111	total: 1.13s	remaining: 35ms
485:	learn: 0.0743912	total: 1.13s	remaining: 32.6ms
486:	learn: 0.0742022	total: 1.14s	remaining: 30.3ms
487:	learn: 0.0739951	total: 1.14s	remaining: 28ms
488:	learn: 0.0737318	total: 1.14s	remaining: 25.6ms
489:	learn: 0.0734557	total: 1.14s	remaining: 23.3ms
490:	learn: 0.0731386	total: 1.14s	remaining: 21ms
491:	learn: 0.0729521	total: 1.15s	remaining: 18.6ms
492:	learn: 0.0726517	total: 1.15s	remaining: 16.3ms
493:	learn: 0.0722558	total: 1.15s	remaining: 14ms
494:	learn: 0.0721393	total: 1.15s	remaining: 11.7ms
495:	learn: 0.0719573	total: 1.16s	remaining: 9.32ms
496:	learn: 0.0717545	total: 1.16s	remaining: 6.99ms
497:	learn: 0.0714763	total: 1.16s	remaining: 4.66ms
498:	learn: 0.0712417	total: 1.16s	remaining: 2.33ms
499:	learn: 0.0710472	total: 1.17s	remaining: 0us
0:	learn: 0.6878136	total: 2.91ms	remaining: 1.45s
1:	learn: 0.6823428	total: 5.53ms	remaining: 1.38s
2:	learn: 0.6766512	total: 7.85ms	remaining: 1.3s
3:	learn: 0.6700773	total: 10.1ms	remaining: 1.25s
4:	learn: 0.6658315	total: 12.4ms	remaining: 1.22s
5:	learn: 0.6607857	total: 15.3ms	remaining: 1.26s
6:	learn: 0.6560992	total: 17.6ms	remaining: 1.24s
7:	learn: 0.6498230	total: 19.8ms	remaining: 1.22s
8:	learn: 0.6443708	total: 22.2ms	remaining: 1.21s
9:	learn: 0.6390019	total: 24.5ms	remaining: 1.2s
10:	learn: 0.6348841	total: 26.7ms	remaining: 1.19s
11:	learn: 0.6305556	total: 29ms	remaining: 1.18s
12:	learn: 0.6255859	total: 31.2ms	remaining: 1.17s
13:	learn: 0.6204373	total: 33.5ms	remaining: 1.16s
14:	learn: 0.6149888	total: 35.8ms	remaining: 1.16s
15:	learn: 0.6113043	total: 38ms	remaining: 1.15s
16:	learn: 0.6066839	total: 40.3ms	remaining: 1.15s
17:	learn: 0.5998365	total: 42.7ms	remaining: 1.14s
18:	learn: 0.5953011	total: 45ms	remaining: 1.14s
19:	learn: 0.5918322	total: 47.2ms	remaining: 1.13s
20:	learn: 0.5874638	total: 49.4ms	remaining: 1.13s
21:	learn: 0.5837567	total: 51.7ms	remaining: 1.12s
22:	learn: 0.5800271	total: 54.1ms	remaining: 1.12s
23:	learn: 0.5754004	total: 56.4ms	remaining: 1.12s
24:	learn: 0.5706558	total: 58.6ms	remaining: 1.11s
25:	learn: 0.5671610	total: 60.9ms	remaining: 1.11s
26:	learn: 0.5626957	total: 63.2ms	remaining: 1.11s
27:	learn: 0.5585954	total: 65.5ms	remaining: 1.1s
28:	learn: 0.5545648	total: 67.8ms	remaining: 1.1s
29:	learn: 0.5505880	total: 70ms	remaining: 1.1s
30:	learn: 0.5482252	total: 72.3ms	remaining: 1.09s
31:	learn: 0.5449014	total: 74.6ms	remaining: 1.09s
32:	learn: 0.5413756	total: 76.8ms	remaining: 1.09s
33:	learn: 0.5376752	total: 79.1ms	remaining: 1.08s
34:	learn: 0.5334653	total: 81.4ms	remaining: 1.08s
35:	learn: 0.5298963	total: 83.7ms	remaining: 1.08s
36:	learn: 0.5259105	total: 85.9ms	remaining: 1.07s
37:	learn: 0.5230090	total: 88.2ms	remaining: 1.07s
38:	learn: 0.5192496	total: 90.5ms	remaining: 1.07s
39:	learn: 0.5156907	total: 92.8ms	remaining: 1.07s
40:	learn: 0.5123705	total: 95.1ms	remaining: 1.06s
41:	learn: 0.5092958	total: 98ms	remaining: 1.07s
42:	learn: 0.5060266	total: 100ms	remaining: 1.07s
43:	learn: 0.5017495	total: 103ms	remaining: 1.06s
44:	learn: 0.4989338	total: 105ms	remaining: 1.06s
45:	learn: 0.4957464	total: 107ms	remaining: 1.06s
46:	learn: 0.4908976	total: 110ms	remaining: 1.06s
47:	learn: 0.4877147	total: 112ms	remaining: 1.05s
48:	learn: 0.4839952	total: 114ms	remaining: 1.05s
49:	learn: 0.4809848	total: 117ms	remaining: 1.05s
50:	learn: 0.4773372	total: 119ms	remaining: 1.05s
51:	learn: 0.4748252	total: 121ms	remaining: 1.04s
52:	learn: 0.4723675	total: 123ms	remaining: 1.04s
53:	learn: 0.4687538	total: 126ms	remaining: 1.04s
54:	learn: 0.4653437	total: 128ms	remaining: 1.04s
55:	learn: 0.4625159	total: 130ms	remaining: 1.03s
56:	learn: 0.4587981	total: 133ms	remaining: 1.03s
57:	learn: 0.4556165	total: 137ms	remaining: 1.04s
58:	learn: 0.4528786	total: 139ms	remaining: 1.04s
59:	learn: 0.4499542	total: 142ms	remaining: 1.04s
60:	learn: 0.4466819	total: 144ms	remaining: 1.04s
61:	learn: 0.4431238	total: 146ms	remaining: 1.03s
62:	learn: 0.4401196	total: 149ms	remaining: 1.03s
63:	learn: 0.4366320	total: 151ms	remaining: 1.03s
64:	learn: 0.4335838	total: 153ms	remaining: 1.02s
65:	learn: 0.4306525	total: 156ms	remaining: 1.02s
66:	learn: 0.4286333	total: 158ms	remaining: 1.02s
67:	learn: 0.4257218	total: 160ms	remaining: 1.02s
68:	learn: 0.4226619	total: 162ms	remaining: 1.01s
69:	learn: 0.4198535	total: 165ms	remaining: 1.01s
70:	learn: 0.4172407	total: 167ms	remaining: 1.01s
71:	learn: 0.4145755	total: 169ms	remaining: 1.01s
72:	learn: 0.4114618	total: 172ms	remaining: 1s
73:	learn: 0.4090196	total: 174ms	remaining: 1s
74:	learn: 0.4070161	total: 176ms	remaining: 998ms
75:	learn: 0.4040856	total: 178ms	remaining: 995ms
76:	learn: 0.4020881	total: 181ms	remaining: 992ms
77:	learn: 0.3997109	total: 183ms	remaining: 989ms
78:	learn: 0.3971539	total: 185ms	remaining: 987ms
79:	learn: 0.3949315	total: 187ms	remaining: 984ms
80:	learn: 0.3929079	total: 190ms	remaining: 981ms
81:	learn: 0.3907871	total: 192ms	remaining: 978ms
82:	learn: 0.3881161	total: 194ms	remaining: 975ms
83:	learn: 0.3855744	total: 196ms	remaining: 973ms
84:	learn: 0.3839051	total: 199ms	remaining: 972ms
85:	learn: 0.3818248	total: 201ms	remaining: 970ms
86:	learn: 0.3795651	total: 204ms	remaining: 967ms
87:	learn: 0.3778921	total: 206ms	remaining: 964ms
88:	learn: 0.3757912	total: 208ms	remaining: 962ms
89:	learn: 0.3737454	total: 211ms	remaining: 959ms
90:	learn: 0.3717791	total: 213ms	remaining: 956ms
91:	learn: 0.3688118	total: 215ms	remaining: 953ms
92:	learn: 0.3664512	total: 217ms	remaining: 950ms
93:	learn: 0.3648723	total: 219ms	remaining: 948ms
94:	learn: 0.3623697	total: 222ms	remaining: 945ms
95:	learn: 0.3603604	total: 224ms	remaining: 943ms
96:	learn: 0.3583567	total: 226ms	remaining: 941ms
97:	learn: 0.3559225	total: 229ms	remaining: 938ms
98:	learn: 0.3545270	total: 231ms	remaining: 936ms
99:	learn: 0.3520597	total: 233ms	remaining: 933ms
100:	learn: 0.3498520	total: 236ms	remaining: 931ms
101:	learn: 0.3483858	total: 238ms	remaining: 928ms
102:	learn: 0.3466285	total: 240ms	remaining: 926ms
103:	learn: 0.3447437	total: 243ms	remaining: 926ms
104:	learn: 0.3434010	total: 246ms	remaining: 924ms
105:	learn: 0.3415935	total: 248ms	remaining: 921ms
106:	learn: 0.3394854	total: 250ms	remaining: 919ms
107:	learn: 0.3377214	total: 252ms	remaining: 916ms
108:	learn: 0.3361360	total: 255ms	remaining: 914ms
109:	learn: 0.3341434	total: 257ms	remaining: 911ms
110:	learn: 0.3315360	total: 259ms	remaining: 909ms
111:	learn: 0.3298405	total: 262ms	remaining: 906ms
112:	learn: 0.3281013	total: 264ms	remaining: 903ms
113:	learn: 0.3261142	total: 266ms	remaining: 901ms
114:	learn: 0.3243745	total: 268ms	remaining: 899ms
115:	learn: 0.3233468	total: 271ms	remaining: 896ms
116:	learn: 0.3213068	total: 273ms	remaining: 894ms
117:	learn: 0.3194338	total: 275ms	remaining: 891ms
118:	learn: 0.3178368	total: 278ms	remaining: 889ms
119:	learn: 0.3159410	total: 280ms	remaining: 886ms
120:	learn: 0.3138475	total: 282ms	remaining: 884ms
121:	learn: 0.3116736	total: 284ms	remaining: 881ms
122:	learn: 0.3101993	total: 287ms	remaining: 879ms
123:	learn: 0.3088385	total: 289ms	remaining: 876ms
124:	learn: 0.3071246	total: 291ms	remaining: 874ms
125:	learn: 0.3055002	total: 294ms	remaining: 871ms
126:	learn: 0.3037617	total: 297ms	remaining: 871ms
127:	learn: 0.3020638	total: 299ms	remaining: 869ms
128:	learn: 0.3007817	total: 301ms	remaining: 866ms
129:	learn: 0.2992144	total: 303ms	remaining: 864ms
130:	learn: 0.2976350	total: 306ms	remaining: 862ms
131:	learn: 0.2960441	total: 308ms	remaining: 859ms
132:	learn: 0.2940112	total: 310ms	remaining: 857ms
133:	learn: 0.2923251	total: 313ms	remaining: 856ms
134:	learn: 0.2912706	total: 316ms	remaining: 855ms
135:	learn: 0.2900718	total: 319ms	remaining: 852ms
136:	learn: 0.2886953	total: 321ms	remaining: 850ms
137:	learn: 0.2873193	total: 323ms	remaining: 847ms
138:	learn: 0.2856317	total: 325ms	remaining: 845ms
139:	learn: 0.2839817	total: 328ms	remaining: 842ms
140:	learn: 0.2829222	total: 330ms	remaining: 839ms
141:	learn: 0.2813702	total: 332ms	remaining: 837ms
142:	learn: 0.2801222	total: 334ms	remaining: 834ms
143:	learn: 0.2789691	total: 336ms	remaining: 832ms
144:	learn: 0.2779322	total: 339ms	remaining: 830ms
145:	learn: 0.2766597	total: 341ms	remaining: 828ms
146:	learn: 0.2758798	total: 344ms	remaining: 825ms
147:	learn: 0.2745080	total: 346ms	remaining: 823ms
148:	learn: 0.2729713	total: 348ms	remaining: 821ms
149:	learn: 0.2716442	total: 351ms	remaining: 818ms
150:	learn: 0.2699517	total: 353ms	remaining: 816ms
151:	learn: 0.2686116	total: 355ms	remaining: 813ms
152:	learn: 0.2671741	total: 357ms	remaining: 811ms
153:	learn: 0.2661257	total: 360ms	remaining: 808ms
154:	learn: 0.2647218	total: 362ms	remaining: 806ms
155:	learn: 0.2637175	total: 364ms	remaining: 803ms
156:	learn: 0.2625277	total: 367ms	remaining: 801ms
157:	learn: 0.2613233	total: 369ms	remaining: 798ms
158:	learn: 0.2604785	total: 371ms	remaining: 796ms
159:	learn: 0.2592113	total: 373ms	remaining: 793ms
160:	learn: 0.2583588	total: 376ms	remaining: 791ms
161:	learn: 0.2571983	total: 378ms	remaining: 788ms
162:	learn: 0.2561451	total: 380ms	remaining: 786ms
163:	learn: 0.2549871	total: 382ms	remaining: 784ms
164:	learn: 0.2541400	total: 385ms	remaining: 781ms
165:	learn: 0.2531392	total: 387ms	remaining: 779ms
166:	learn: 0.2518743	total: 389ms	remaining: 776ms
167:	learn: 0.2502837	total: 392ms	remaining: 774ms
168:	learn: 0.2493030	total: 394ms	remaining: 772ms
169:	learn: 0.2483895	total: 397ms	remaining: 770ms
170:	learn: 0.2472463	total: 399ms	remaining: 767ms
171:	learn: 0.2462184	total: 401ms	remaining: 765ms
172:	learn: 0.2449741	total: 403ms	remaining: 763ms
173:	learn: 0.2437496	total: 406ms	remaining: 760ms
174:	learn: 0.2424889	total: 408ms	remaining: 758ms
175:	learn: 0.2412445	total: 410ms	remaining: 755ms
176:	learn: 0.2401787	total: 412ms	remaining: 753ms
177:	learn: 0.2387819	total: 415ms	remaining: 750ms
178:	learn: 0.2377818	total: 417ms	remaining: 748ms
179:	learn: 0.2364607	total: 419ms	remaining: 745ms
180:	learn: 0.2350843	total: 422ms	remaining: 743ms
181:	learn: 0.2340027	total: 424ms	remaining: 741ms
182:	learn: 0.2329750	total: 426ms	remaining: 738ms
183:	learn: 0.2318278	total: 428ms	remaining: 736ms
184:	learn: 0.2309968	total: 431ms	remaining: 734ms
185:	learn: 0.2298405	total: 433ms	remaining: 731ms
186:	learn: 0.2286233	total: 438ms	remaining: 733ms
187:	learn: 0.2273380	total: 440ms	remaining: 730ms
188:	learn: 0.2260601	total: 442ms	remaining: 728ms
189:	learn: 0.2250348	total: 445ms	remaining: 725ms
190:	learn: 0.2242646	total: 447ms	remaining: 723ms
191:	learn: 0.2232914	total: 449ms	remaining: 721ms
192:	learn: 0.2224279	total: 452ms	remaining: 718ms
193:	learn: 0.2216081	total: 454ms	remaining: 716ms
194:	learn: 0.2206674	total: 456ms	remaining: 713ms
195:	learn: 0.2197799	total: 458ms	remaining: 711ms
196:	learn: 0.2190800	total: 461ms	remaining: 708ms
197:	learn: 0.2182528	total: 463ms	remaining: 706ms
198:	learn: 0.2171229	total: 465ms	remaining: 704ms
199:	learn: 0.2161967	total: 468ms	remaining: 701ms
200:	learn: 0.2151905	total: 470ms	remaining: 699ms
201:	learn: 0.2146097	total: 472ms	remaining: 697ms
202:	learn: 0.2137379	total: 474ms	remaining: 694ms
203:	learn: 0.2128558	total: 477ms	remaining: 692ms
204:	learn: 0.2120411	total: 479ms	remaining: 689ms
205:	learn: 0.2111809	total: 481ms	remaining: 687ms
206:	learn: 0.2103066	total: 484ms	remaining: 685ms
207:	learn: 0.2091970	total: 486ms	remaining: 682ms
208:	learn: 0.2078118	total: 488ms	remaining: 680ms
209:	learn: 0.2073343	total: 491ms	remaining: 677ms
210:	learn: 0.2064654	total: 493ms	remaining: 675ms
211:	learn: 0.2058209	total: 495ms	remaining: 673ms
212:	learn: 0.2047430	total: 498ms	remaining: 670ms
213:	learn: 0.2042579	total: 500ms	remaining: 668ms
214:	learn: 0.2035323	total: 502ms	remaining: 666ms
215:	learn: 0.2028871	total: 504ms	remaining: 663ms
216:	learn: 0.2022150	total: 507ms	remaining: 661ms
217:	learn: 0.2012783	total: 509ms	remaining: 658ms
218:	learn: 0.2002383	total: 511ms	remaining: 656ms
219:	learn: 0.1993363	total: 514ms	remaining: 654ms
220:	learn: 0.1986132	total: 516ms	remaining: 651ms
221:	learn: 0.1976843	total: 518ms	remaining: 649ms
222:	learn: 0.1968060	total: 521ms	remaining: 648ms
223:	learn: 0.1958277	total: 524ms	remaining: 645ms
224:	learn: 0.1947343	total: 526ms	remaining: 643ms
225:	learn: 0.1940220	total: 528ms	remaining: 640ms
226:	learn: 0.1934493	total: 531ms	remaining: 638ms
227:	learn: 0.1927212	total: 534ms	remaining: 637ms
228:	learn: 0.1920639	total: 536ms	remaining: 635ms
229:	learn: 0.1912004	total: 539ms	remaining: 632ms
230:	learn: 0.1906399	total: 541ms	remaining: 630ms
231:	learn: 0.1899425	total: 543ms	remaining: 628ms
232:	learn: 0.1888705	total: 546ms	remaining: 625ms
233:	learn: 0.1879444	total: 548ms	remaining: 623ms
234:	learn: 0.1871429	total: 550ms	remaining: 620ms
235:	learn: 0.1863465	total: 552ms	remaining: 618ms
236:	learn: 0.1856773	total: 555ms	remaining: 616ms
237:	learn: 0.1848272	total: 557ms	remaining: 613ms
238:	learn: 0.1840367	total: 559ms	remaining: 611ms
239:	learn: 0.1831051	total: 562ms	remaining: 608ms
240:	learn: 0.1821821	total: 564ms	remaining: 606ms
241:	learn: 0.1813632	total: 566ms	remaining: 603ms
242:	learn: 0.1806308	total: 568ms	remaining: 601ms
243:	learn: 0.1797810	total: 571ms	remaining: 599ms
244:	learn: 0.1788471	total: 573ms	remaining: 596ms
245:	learn: 0.1782619	total: 575ms	remaining: 594ms
246:	learn: 0.1775910	total: 578ms	remaining: 592ms
247:	learn: 0.1767072	total: 580ms	remaining: 589ms
248:	learn: 0.1760478	total: 582ms	remaining: 587ms
249:	learn: 0.1752748	total: 584ms	remaining: 584ms
250:	learn: 0.1743254	total: 587ms	remaining: 582ms
251:	learn: 0.1738410	total: 589ms	remaining: 580ms
252:	learn: 0.1732492	total: 592ms	remaining: 577ms
253:	learn: 0.1724875	total: 594ms	remaining: 575ms
254:	learn: 0.1718257	total: 596ms	remaining: 573ms
255:	learn: 0.1712618	total: 599ms	remaining: 571ms
256:	learn: 0.1706496	total: 602ms	remaining: 569ms
257:	learn: 0.1698991	total: 605ms	remaining: 567ms
258:	learn: 0.1692590	total: 607ms	remaining: 565ms
259:	learn: 0.1685683	total: 609ms	remaining: 563ms
260:	learn: 0.1679500	total: 612ms	remaining: 560ms
261:	learn: 0.1671837	total: 614ms	remaining: 558ms
262:	learn: 0.1665881	total: 617ms	remaining: 556ms
263:	learn: 0.1660451	total: 619ms	remaining: 553ms
264:	learn: 0.1652085	total: 622ms	remaining: 551ms
265:	learn: 0.1645460	total: 624ms	remaining: 549ms
266:	learn: 0.1640515	total: 626ms	remaining: 547ms
267:	learn: 0.1634237	total: 631ms	remaining: 546ms
268:	learn: 0.1627889	total: 633ms	remaining: 544ms
269:	learn: 0.1619948	total: 636ms	remaining: 542ms
270:	learn: 0.1615225	total: 638ms	remaining: 539ms
271:	learn: 0.1610163	total: 641ms	remaining: 537ms
272:	learn: 0.1603218	total: 643ms	remaining: 535ms
273:	learn: 0.1595096	total: 646ms	remaining: 533ms
274:	learn: 0.1589765	total: 648ms	remaining: 530ms
275:	learn: 0.1585119	total: 651ms	remaining: 528ms
276:	learn: 0.1579506	total: 653ms	remaining: 526ms
277:	learn: 0.1571900	total: 655ms	remaining: 523ms
278:	learn: 0.1563126	total: 658ms	remaining: 521ms
279:	learn: 0.1559184	total: 661ms	remaining: 519ms
280:	learn: 0.1554169	total: 663ms	remaining: 517ms
281:	learn: 0.1547372	total: 666ms	remaining: 514ms
282:	learn: 0.1541444	total: 668ms	remaining: 512ms
283:	learn: 0.1535318	total: 670ms	remaining: 510ms
284:	learn: 0.1529361	total: 673ms	remaining: 508ms
285:	learn: 0.1522474	total: 675ms	remaining: 505ms
286:	learn: 0.1516782	total: 678ms	remaining: 503ms
287:	learn: 0.1506326	total: 680ms	remaining: 501ms
288:	learn: 0.1499877	total: 682ms	remaining: 498ms
289:	learn: 0.1492256	total: 685ms	remaining: 496ms
290:	learn: 0.1486042	total: 687ms	remaining: 494ms
291:	learn: 0.1479728	total: 690ms	remaining: 491ms
292:	learn: 0.1472866	total: 693ms	remaining: 489ms
293:	learn: 0.1467039	total: 695ms	remaining: 487ms
294:	learn: 0.1461563	total: 698ms	remaining: 485ms
295:	learn: 0.1456704	total: 700ms	remaining: 482ms
296:	learn: 0.1448970	total: 703ms	remaining: 480ms
297:	learn: 0.1445525	total: 705ms	remaining: 478ms
298:	learn: 0.1440390	total: 707ms	remaining: 476ms
299:	learn: 0.1434057	total: 710ms	remaining: 473ms
300:	learn: 0.1428842	total: 712ms	remaining: 471ms
301:	learn: 0.1423328	total: 715ms	remaining: 469ms
302:	learn: 0.1418687	total: 718ms	remaining: 467ms
303:	learn: 0.1413429	total: 720ms	remaining: 464ms
304:	learn: 0.1408490	total: 722ms	remaining: 462ms
305:	learn: 0.1403441	total: 726ms	remaining: 460ms
306:	learn: 0.1398163	total: 728ms	remaining: 458ms
307:	learn: 0.1392797	total: 730ms	remaining: 455ms
308:	learn: 0.1387763	total: 733ms	remaining: 453ms
309:	learn: 0.1382613	total: 735ms	remaining: 450ms
310:	learn: 0.1376940	total: 737ms	remaining: 448ms
311:	learn: 0.1371619	total: 740ms	remaining: 446ms
312:	learn: 0.1364984	total: 742ms	remaining: 443ms
313:	learn: 0.1358965	total: 744ms	remaining: 441ms
314:	learn: 0.1354005	total: 747ms	remaining: 439ms
315:	learn: 0.1349167	total: 749ms	remaining: 436ms
316:	learn: 0.1342110	total: 752ms	remaining: 434ms
317:	learn: 0.1336481	total: 755ms	remaining: 432ms
318:	learn: 0.1331136	total: 757ms	remaining: 430ms
319:	learn: 0.1325062	total: 760ms	remaining: 427ms
320:	learn: 0.1321263	total: 762ms	remaining: 425ms
321:	learn: 0.1317681	total: 765ms	remaining: 423ms
322:	learn: 0.1312862	total: 767ms	remaining: 420ms
323:	learn: 0.1309897	total: 769ms	remaining: 418ms
324:	learn: 0.1306339	total: 772ms	remaining: 415ms
325:	learn: 0.1303879	total: 774ms	remaining: 413ms
326:	learn: 0.1299691	total: 777ms	remaining: 411ms
327:	learn: 0.1295421	total: 779ms	remaining: 409ms
328:	learn: 0.1290238	total: 782ms	remaining: 406ms
329:	learn: 0.1286330	total: 784ms	remaining: 404ms
330:	learn: 0.1281245	total: 787ms	remaining: 402ms
331:	learn: 0.1276437	total: 789ms	remaining: 399ms
332:	learn: 0.1273966	total: 792ms	remaining: 397ms
333:	learn: 0.1270748	total: 794ms	remaining: 395ms
334:	learn: 0.1266012	total: 797ms	remaining: 392ms
335:	learn: 0.1259851	total: 799ms	remaining: 390ms
336:	learn: 0.1253552	total: 802ms	remaining: 388ms
337:	learn: 0.1248686	total: 804ms	remaining: 385ms
338:	learn: 0.1244381	total: 806ms	remaining: 383ms
339:	learn: 0.1241499	total: 808ms	remaining: 380ms
340:	learn: 0.1236742	total: 811ms	remaining: 378ms
341:	learn: 0.1232312	total: 813ms	remaining: 376ms
342:	learn: 0.1227252	total: 816ms	remaining: 373ms
343:	learn: 0.1223525	total: 818ms	remaining: 371ms
344:	learn: 0.1219963	total: 824ms	remaining: 370ms
345:	learn: 0.1216307	total: 826ms	remaining: 368ms
346:	learn: 0.1212401	total: 829ms	remaining: 365ms
347:	learn: 0.1207466	total: 831ms	remaining: 363ms
348:	learn: 0.1201818	total: 834ms	remaining: 361ms
349:	learn: 0.1197137	total: 836ms	remaining: 358ms
350:	learn: 0.1194865	total: 839ms	remaining: 356ms
351:	learn: 0.1190959	total: 841ms	remaining: 354ms
352:	learn: 0.1187246	total: 843ms	remaining: 351ms
353:	learn: 0.1183851	total: 846ms	remaining: 349ms
354:	learn: 0.1180011	total: 848ms	remaining: 346ms
355:	learn: 0.1174635	total: 850ms	remaining: 344ms
356:	learn: 0.1170585	total: 852ms	remaining: 341ms
357:	learn: 0.1167112	total: 855ms	remaining: 339ms
358:	learn: 0.1164501	total: 857ms	remaining: 337ms
359:	learn: 0.1160615	total: 859ms	remaining: 334ms
360:	learn: 0.1156244	total: 862ms	remaining: 332ms
361:	learn: 0.1152482	total: 864ms	remaining: 329ms
362:	learn: 0.1149839	total: 867ms	remaining: 327ms
363:	learn: 0.1146967	total: 869ms	remaining: 325ms
364:	learn: 0.1143266	total: 871ms	remaining: 322ms
365:	learn: 0.1139238	total: 874ms	remaining: 320ms
366:	learn: 0.1135189	total: 876ms	remaining: 317ms
367:	learn: 0.1131537	total: 878ms	remaining: 315ms
368:	learn: 0.1127708	total: 881ms	remaining: 313ms
369:	learn: 0.1123952	total: 883ms	remaining: 310ms
370:	learn: 0.1120608	total: 885ms	remaining: 308ms
371:	learn: 0.1116850	total: 888ms	remaining: 305ms
372:	learn: 0.1112097	total: 890ms	remaining: 303ms
373:	learn: 0.1106985	total: 892ms	remaining: 301ms
374:	learn: 0.1103471	total: 894ms	remaining: 298ms
375:	learn: 0.1099850	total: 897ms	remaining: 296ms
376:	learn: 0.1095426	total: 899ms	remaining: 293ms
377:	learn: 0.1091868	total: 901ms	remaining: 291ms
378:	learn: 0.1086831	total: 904ms	remaining: 288ms
379:	learn: 0.1082939	total: 906ms	remaining: 286ms
380:	learn: 0.1080338	total: 908ms	remaining: 284ms
381:	learn: 0.1077519	total: 911ms	remaining: 281ms
382:	learn: 0.1072649	total: 914ms	remaining: 279ms
383:	learn: 0.1068723	total: 916ms	remaining: 277ms
384:	learn: 0.1064934	total: 919ms	remaining: 275ms
385:	learn: 0.1059531	total: 922ms	remaining: 272ms
386:	learn: 0.1056296	total: 924ms	remaining: 270ms
387:	learn: 0.1053066	total: 926ms	remaining: 267ms
388:	learn: 0.1047711	total: 929ms	remaining: 265ms
389:	learn: 0.1044918	total: 931ms	remaining: 263ms
390:	learn: 0.1041163	total: 933ms	remaining: 260ms
391:	learn: 0.1037910	total: 936ms	remaining: 258ms
392:	learn: 0.1034736	total: 938ms	remaining: 255ms
393:	learn: 0.1031685	total: 940ms	remaining: 253ms
394:	learn: 0.1029014	total: 943ms	remaining: 251ms
395:	learn: 0.1025038	total: 945ms	remaining: 248ms
396:	learn: 0.1021310	total: 947ms	remaining: 246ms
397:	learn: 0.1018185	total: 949ms	remaining: 243ms
398:	learn: 0.1014979	total: 952ms	remaining: 241ms
399:	learn: 0.1011858	total: 954ms	remaining: 239ms
400:	learn: 0.1008243	total: 957ms	remaining: 236ms
401:	learn: 0.1005385	total: 959ms	remaining: 234ms
402:	learn: 0.1001345	total: 962ms	remaining: 231ms
403:	learn: 0.0998638	total: 964ms	remaining: 229ms
404:	learn: 0.0994813	total: 966ms	remaining: 227ms
405:	learn: 0.0991298	total: 969ms	remaining: 224ms
406:	learn: 0.0988972	total: 971ms	remaining: 222ms
407:	learn: 0.0985809	total: 973ms	remaining: 219ms
408:	learn: 0.0981945	total: 975ms	remaining: 217ms
409:	learn: 0.0978475	total: 979ms	remaining: 215ms
410:	learn: 0.0974436	total: 981ms	remaining: 213ms
411:	learn: 0.0970199	total: 984ms	remaining: 210ms
412:	learn: 0.0967135	total: 986ms	remaining: 208ms
413:	learn: 0.0963362	total: 988ms	remaining: 205ms
414:	learn: 0.0961141	total: 991ms	remaining: 203ms
415:	learn: 0.0959059	total: 993ms	remaining: 201ms
416:	learn: 0.0955875	total: 996ms	remaining: 198ms
417:	learn: 0.0952738	total: 998ms	remaining: 196ms
418:	learn: 0.0950349	total: 1s	remaining: 193ms
419:	learn: 0.0947758	total: 1s	remaining: 191ms
420:	learn: 0.0945783	total: 1s	remaining: 189ms
421:	learn: 0.0941439	total: 1.01s	remaining: 186ms
422:	learn: 0.0937330	total: 1.01s	remaining: 184ms
423:	learn: 0.0933426	total: 1.01s	remaining: 182ms
424:	learn: 0.0929742	total: 1.02s	remaining: 180ms
425:	learn: 0.0926348	total: 1.02s	remaining: 177ms
426:	learn: 0.0922174	total: 1.02s	remaining: 175ms
427:	learn: 0.0918688	total: 1.02s	remaining: 172ms
428:	learn: 0.0915235	total: 1.03s	remaining: 170ms
429:	learn: 0.0911555	total: 1.03s	remaining: 168ms
430:	learn: 0.0908858	total: 1.03s	remaining: 165ms
431:	learn: 0.0906099	total: 1.03s	remaining: 163ms
432:	learn: 0.0902574	total: 1.04s	remaining: 160ms
433:	learn: 0.0897820	total: 1.04s	remaining: 158ms
434:	learn: 0.0894883	total: 1.04s	remaining: 156ms
435:	learn: 0.0892551	total: 1.04s	remaining: 153ms
436:	learn: 0.0889610	total: 1.04s	remaining: 151ms
437:	learn: 0.0886721	total: 1.05s	remaining: 148ms
438:	learn: 0.0883326	total: 1.05s	remaining: 146ms
439:	learn: 0.0881016	total: 1.05s	remaining: 144ms
440:	learn: 0.0877827	total: 1.05s	remaining: 141ms
441:	learn: 0.0874886	total: 1.06s	remaining: 139ms
442:	learn: 0.0872020	total: 1.06s	remaining: 136ms
443:	learn: 0.0868312	total: 1.06s	remaining: 134ms
444:	learn: 0.0865276	total: 1.06s	remaining: 132ms
445:	learn: 0.0862587	total: 1.07s	remaining: 129ms
446:	learn: 0.0859994	total: 1.07s	remaining: 127ms
447:	learn: 0.0857720	total: 1.07s	remaining: 124ms
448:	learn: 0.0853614	total: 1.07s	remaining: 122ms
449:	learn: 0.0850385	total: 1.08s	remaining: 120ms
450:	learn: 0.0847952	total: 1.08s	remaining: 117ms
451:	learn: 0.0845151	total: 1.08s	remaining: 115ms
452:	learn: 0.0842478	total: 1.08s	remaining: 112ms
453:	learn: 0.0840103	total: 1.08s	remaining: 110ms
454:	learn: 0.0837923	total: 1.09s	remaining: 108ms
455:	learn: 0.0835321	total: 1.09s	remaining: 105ms
456:	learn: 0.0832891	total: 1.09s	remaining: 103ms
457:	learn: 0.0830369	total: 1.09s	remaining: 100ms
458:	learn: 0.0827145	total: 1.1s	remaining: 98ms
459:	learn: 0.0824122	total: 1.1s	remaining: 95.6ms
460:	learn: 0.0821621	total: 1.1s	remaining: 93.2ms
461:	learn: 0.0817996	total: 1.1s	remaining: 90.8ms
462:	learn: 0.0814227	total: 1.11s	remaining: 88.4ms
463:	learn: 0.0811579	total: 1.11s	remaining: 86ms
464:	learn: 0.0809766	total: 1.11s	remaining: 83.9ms
465:	learn: 0.0806873	total: 1.12s	remaining: 81.5ms
466:	learn: 0.0804079	total: 1.12s	remaining: 79.1ms
467:	learn: 0.0801525	total: 1.12s	remaining: 76.7ms
468:	learn: 0.0798769	total: 1.12s	remaining: 74.3ms
469:	learn: 0.0794651	total: 1.13s	remaining: 71.9ms
470:	learn: 0.0792042	total: 1.13s	remaining: 69.5ms
471:	learn: 0.0789552	total: 1.13s	remaining: 67.1ms
472:	learn: 0.0786195	total: 1.13s	remaining: 64.7ms
473:	learn: 0.0784578	total: 1.14s	remaining: 62.3ms
474:	learn: 0.0781765	total: 1.14s	remaining: 59.9ms
475:	learn: 0.0780504	total: 1.14s	remaining: 57.5ms
476:	learn: 0.0777781	total: 1.14s	remaining: 55.1ms
477:	learn: 0.0775377	total: 1.15s	remaining: 52.7ms
478:	learn: 0.0772482	total: 1.15s	remaining: 50.3ms
479:	learn: 0.0769291	total: 1.15s	remaining: 47.9ms
480:	learn: 0.0767321	total: 1.15s	remaining: 45.5ms
481:	learn: 0.0764309	total: 1.15s	remaining: 43.1ms
482:	learn: 0.0761167	total: 1.16s	remaining: 40.7ms
483:	learn: 0.0758234	total: 1.16s	remaining: 38.3ms
484:	learn: 0.0756095	total: 1.16s	remaining: 35.9ms
485:	learn: 0.0753521	total: 1.16s	remaining: 33.5ms
486:	learn: 0.0751170	total: 1.17s	remaining: 31.1ms
487:	learn: 0.0749183	total: 1.17s	remaining: 28.7ms
488:	learn: 0.0747257	total: 1.17s	remaining: 26.3ms
489:	learn: 0.0744158	total: 1.17s	remaining: 23.9ms
490:	learn: 0.0741194	total: 1.18s	remaining: 21.6ms
491:	learn: 0.0738647	total: 1.18s	remaining: 19.2ms
492:	learn: 0.0736206	total: 1.18s	remaining: 16.8ms
493:	learn: 0.0734159	total: 1.18s	remaining: 14.4ms
494:	learn: 0.0731953	total: 1.19s	remaining: 12ms
495:	learn: 0.0730070	total: 1.19s	remaining: 9.58ms
496:	learn: 0.0727517	total: 1.19s	remaining: 7.18ms
497:	learn: 0.0725476	total: 1.19s	remaining: 4.79ms
498:	learn: 0.0723337	total: 1.2s	remaining: 2.4ms
499:	learn: 0.0720303	total: 1.2s	remaining: 0us
0:	learn: 0.6871655	total: 3.11ms	remaining: 1.55s
1:	learn: 0.6817908	total: 5.45ms	remaining: 1.36s
2:	learn: 0.6768791	total: 7.71ms	remaining: 1.28s
3:	learn: 0.6718047	total: 10ms	remaining: 1.24s
4:	learn: 0.6662876	total: 12.3ms	remaining: 1.22s
5:	learn: 0.6603939	total: 14.7ms	remaining: 1.21s
6:	learn: 0.6551185	total: 20.5ms	remaining: 1.44s
7:	learn: 0.6497461	total: 22.8ms	remaining: 1.4s
8:	learn: 0.6440786	total: 25.1ms	remaining: 1.37s
9:	learn: 0.6396478	total: 27.4ms	remaining: 1.34s
10:	learn: 0.6328598	total: 29.6ms	remaining: 1.32s
11:	learn: 0.6282164	total: 31.9ms	remaining: 1.3s
12:	learn: 0.6247445	total: 34.2ms	remaining: 1.28s
13:	learn: 0.6187690	total: 36.5ms	remaining: 1.27s
14:	learn: 0.6143054	total: 38.7ms	remaining: 1.25s
15:	learn: 0.6087531	total: 40.9ms	remaining: 1.24s
16:	learn: 0.6045841	total: 43.2ms	remaining: 1.23s
17:	learn: 0.6005366	total: 45.5ms	remaining: 1.22s
18:	learn: 0.5961040	total: 47.7ms	remaining: 1.21s
19:	learn: 0.5927258	total: 49.9ms	remaining: 1.2s
20:	learn: 0.5885026	total: 52.1ms	remaining: 1.19s
21:	learn: 0.5834228	total: 54.4ms	remaining: 1.18s
22:	learn: 0.5796660	total: 56.7ms	remaining: 1.18s
23:	learn: 0.5756777	total: 59ms	remaining: 1.17s
24:	learn: 0.5720639	total: 61.3ms	remaining: 1.17s
25:	learn: 0.5683599	total: 63.5ms	remaining: 1.16s
26:	learn: 0.5630732	total: 65.8ms	remaining: 1.15s
27:	learn: 0.5594726	total: 68ms	remaining: 1.15s
28:	learn: 0.5553599	total: 70.3ms	remaining: 1.14s
29:	learn: 0.5509136	total: 72.7ms	remaining: 1.14s
30:	learn: 0.5475352	total: 74.9ms	remaining: 1.13s
31:	learn: 0.5447377	total: 77.1ms	remaining: 1.13s
32:	learn: 0.5421123	total: 79.3ms	remaining: 1.12s
33:	learn: 0.5381164	total: 81.7ms	remaining: 1.12s
34:	learn: 0.5339469	total: 84.1ms	remaining: 1.12s
35:	learn: 0.5301203	total: 86.4ms	remaining: 1.11s
36:	learn: 0.5264941	total: 88.7ms	remaining: 1.11s
37:	learn: 0.5230928	total: 91ms	remaining: 1.11s
38:	learn: 0.5199855	total: 93.3ms	remaining: 1.1s
39:	learn: 0.5170107	total: 95.6ms	remaining: 1.1s
40:	learn: 0.5140895	total: 97.7ms	remaining: 1.09s
41:	learn: 0.5114053	total: 100ms	remaining: 1.09s
42:	learn: 0.5089123	total: 102ms	remaining: 1.09s
43:	learn: 0.5048698	total: 104ms	remaining: 1.08s
44:	learn: 0.5011829	total: 107ms	remaining: 1.08s
45:	learn: 0.4985540	total: 109ms	remaining: 1.07s
46:	learn: 0.4945522	total: 111ms	remaining: 1.07s
47:	learn: 0.4908369	total: 114ms	remaining: 1.07s
48:	learn: 0.4872136	total: 116ms	remaining: 1.07s
49:	learn: 0.4840854	total: 118ms	remaining: 1.06s
50:	learn: 0.4811709	total: 120ms	remaining: 1.06s
51:	learn: 0.4780975	total: 123ms	remaining: 1.06s
52:	learn: 0.4752425	total: 126ms	remaining: 1.06s
53:	learn: 0.4715721	total: 128ms	remaining: 1.06s
54:	learn: 0.4677770	total: 131ms	remaining: 1.06s
55:	learn: 0.4647895	total: 133ms	remaining: 1.05s
56:	learn: 0.4625011	total: 135ms	remaining: 1.05s
57:	learn: 0.4596635	total: 137ms	remaining: 1.05s
58:	learn: 0.4569757	total: 140ms	remaining: 1.04s
59:	learn: 0.4539862	total: 142ms	remaining: 1.04s
60:	learn: 0.4517543	total: 144ms	remaining: 1.04s
61:	learn: 0.4490575	total: 147ms	remaining: 1.04s
62:	learn: 0.4453916	total: 149ms	remaining: 1.03s
63:	learn: 0.4428766	total: 152ms	remaining: 1.03s
64:	learn: 0.4394795	total: 154ms	remaining: 1.03s
65:	learn: 0.4369333	total: 157ms	remaining: 1.03s
66:	learn: 0.4342646	total: 160ms	remaining: 1.03s
67:	learn: 0.4319709	total: 163ms	remaining: 1.03s
68:	learn: 0.4291554	total: 165ms	remaining: 1.03s
69:	learn: 0.4269133	total: 168ms	remaining: 1.03s
70:	learn: 0.4246584	total: 170ms	remaining: 1.03s
71:	learn: 0.4217415	total: 172ms	remaining: 1.02s
72:	learn: 0.4191238	total: 175ms	remaining: 1.02s
73:	learn: 0.4169999	total: 177ms	remaining: 1.02s
74:	learn: 0.4143940	total: 179ms	remaining: 1.01s
75:	learn: 0.4111453	total: 181ms	remaining: 1.01s
76:	learn: 0.4081638	total: 184ms	remaining: 1.01s
77:	learn: 0.4051326	total: 186ms	remaining: 1.01s
78:	learn: 0.4028677	total: 188ms	remaining: 1s
79:	learn: 0.4009272	total: 191ms	remaining: 1s
80:	learn: 0.3988731	total: 193ms	remaining: 997ms
81:	learn: 0.3963161	total: 195ms	remaining: 994ms
82:	learn: 0.3938963	total: 197ms	remaining: 992ms
83:	learn: 0.3908694	total: 200ms	remaining: 989ms
84:	learn: 0.3885247	total: 202ms	remaining: 986ms
85:	learn: 0.3861263	total: 204ms	remaining: 984ms
86:	learn: 0.3832337	total: 207ms	remaining: 981ms
87:	learn: 0.3812639	total: 209ms	remaining: 978ms
88:	learn: 0.3794903	total: 211ms	remaining: 976ms
89:	learn: 0.3771338	total: 214ms	remaining: 973ms
90:	learn: 0.3756723	total: 216ms	remaining: 970ms
91:	learn: 0.3729882	total: 218ms	remaining: 967ms
92:	learn: 0.3710186	total: 220ms	remaining: 965ms
93:	learn: 0.3690944	total: 223ms	remaining: 962ms
94:	learn: 0.3670162	total: 225ms	remaining: 959ms
95:	learn: 0.3654531	total: 228ms	remaining: 958ms
96:	learn: 0.3630421	total: 230ms	remaining: 955ms
97:	learn: 0.3611023	total: 232ms	remaining: 952ms
98:	learn: 0.3598561	total: 234ms	remaining: 949ms
99:	learn: 0.3574516	total: 237ms	remaining: 946ms
100:	learn: 0.3557667	total: 239ms	remaining: 944ms
101:	learn: 0.3543755	total: 241ms	remaining: 941ms
102:	learn: 0.3523735	total: 243ms	remaining: 938ms
103:	learn: 0.3503049	total: 246ms	remaining: 936ms
104:	learn: 0.3489035	total: 248ms	remaining: 934ms
105:	learn: 0.3467716	total: 253ms	remaining: 941ms
106:	learn: 0.3444717	total: 255ms	remaining: 938ms
107:	learn: 0.3425250	total: 258ms	remaining: 935ms
108:	learn: 0.3405457	total: 260ms	remaining: 932ms
109:	learn: 0.3388322	total: 262ms	remaining: 929ms
110:	learn: 0.3364756	total: 264ms	remaining: 927ms
111:	learn: 0.3343220	total: 267ms	remaining: 925ms
112:	learn: 0.3326659	total: 269ms	remaining: 922ms
113:	learn: 0.3307485	total: 271ms	remaining: 919ms
114:	learn: 0.3288697	total: 274ms	remaining: 916ms
115:	learn: 0.3278185	total: 276ms	remaining: 914ms
116:	learn: 0.3263685	total: 278ms	remaining: 911ms
117:	learn: 0.3252694	total: 281ms	remaining: 908ms
118:	learn: 0.3243355	total: 283ms	remaining: 905ms
119:	learn: 0.3229622	total: 285ms	remaining: 903ms
120:	learn: 0.3212609	total: 287ms	remaining: 900ms
121:	learn: 0.3193757	total: 290ms	remaining: 897ms
122:	learn: 0.3172861	total: 292ms	remaining: 895ms
123:	learn: 0.3158639	total: 294ms	remaining: 892ms
124:	learn: 0.3139897	total: 296ms	remaining: 889ms
125:	learn: 0.3122860	total: 299ms	remaining: 886ms
126:	learn: 0.3106830	total: 301ms	remaining: 884ms
127:	learn: 0.3095339	total: 303ms	remaining: 881ms
128:	learn: 0.3082872	total: 306ms	remaining: 879ms
129:	learn: 0.3068111	total: 308ms	remaining: 876ms
130:	learn: 0.3054255	total: 310ms	remaining: 874ms
131:	learn: 0.3038942	total: 312ms	remaining: 871ms
132:	learn: 0.3024197	total: 315ms	remaining: 868ms
133:	learn: 0.3012510	total: 317ms	remaining: 866ms
134:	learn: 0.2996688	total: 320ms	remaining: 864ms
135:	learn: 0.2982007	total: 322ms	remaining: 862ms
136:	learn: 0.2963604	total: 325ms	remaining: 860ms
137:	learn: 0.2947371	total: 327ms	remaining: 858ms
138:	learn: 0.2931846	total: 329ms	remaining: 855ms
139:	learn: 0.2913559	total: 331ms	remaining: 852ms
140:	learn: 0.2904537	total: 334ms	remaining: 850ms
141:	learn: 0.2892857	total: 336ms	remaining: 847ms
142:	learn: 0.2881050	total: 338ms	remaining: 844ms
143:	learn: 0.2870906	total: 341ms	remaining: 842ms
144:	learn: 0.2859259	total: 343ms	remaining: 839ms
145:	learn: 0.2841248	total: 345ms	remaining: 837ms
146:	learn: 0.2832318	total: 350ms	remaining: 841ms
147:	learn: 0.2817524	total: 353ms	remaining: 838ms
148:	learn: 0.2807540	total: 355ms	remaining: 837ms
149:	learn: 0.2796111	total: 358ms	remaining: 834ms
150:	learn: 0.2780288	total: 360ms	remaining: 832ms
151:	learn: 0.2768618	total: 363ms	remaining: 830ms
152:	learn: 0.2753513	total: 365ms	remaining: 828ms
153:	learn: 0.2741423	total: 367ms	remaining: 825ms
154:	learn: 0.2726622	total: 369ms	remaining: 822ms
155:	learn: 0.2714841	total: 372ms	remaining: 819ms
156:	learn: 0.2701848	total: 374ms	remaining: 817ms
157:	learn: 0.2684490	total: 376ms	remaining: 814ms
158:	learn: 0.2674472	total: 378ms	remaining: 811ms
159:	learn: 0.2663933	total: 380ms	remaining: 809ms
160:	learn: 0.2653883	total: 383ms	remaining: 806ms
161:	learn: 0.2641896	total: 385ms	remaining: 804ms
162:	learn: 0.2623466	total: 387ms	remaining: 801ms
163:	learn: 0.2609188	total: 390ms	remaining: 799ms
164:	learn: 0.2597980	total: 392ms	remaining: 796ms
165:	learn: 0.2586373	total: 394ms	remaining: 793ms
166:	learn: 0.2573736	total: 397ms	remaining: 791ms
167:	learn: 0.2560449	total: 399ms	remaining: 788ms
168:	learn: 0.2548604	total: 401ms	remaining: 786ms
169:	learn: 0.2539117	total: 404ms	remaining: 783ms
170:	learn: 0.2526570	total: 406ms	remaining: 781ms
171:	learn: 0.2514588	total: 408ms	remaining: 778ms
172:	learn: 0.2503684	total: 410ms	remaining: 776ms
173:	learn: 0.2495263	total: 413ms	remaining: 773ms
174:	learn: 0.2482718	total: 415ms	remaining: 771ms
175:	learn: 0.2475182	total: 417ms	remaining: 768ms
176:	learn: 0.2465868	total: 419ms	remaining: 765ms
177:	learn: 0.2453726	total: 422ms	remaining: 763ms
178:	learn: 0.2444093	total: 424ms	remaining: 761ms
179:	learn: 0.2433010	total: 426ms	remaining: 758ms
180:	learn: 0.2422526	total: 429ms	remaining: 756ms
181:	learn: 0.2409863	total: 431ms	remaining: 753ms
182:	learn: 0.2398931	total: 433ms	remaining: 751ms
183:	learn: 0.2390738	total: 436ms	remaining: 749ms
184:	learn: 0.2381440	total: 438ms	remaining: 746ms
185:	learn: 0.2373073	total: 441ms	remaining: 744ms
186:	learn: 0.2362944	total: 443ms	remaining: 741ms
187:	learn: 0.2352289	total: 447ms	remaining: 741ms
188:	learn: 0.2341810	total: 449ms	remaining: 739ms
189:	learn: 0.2333567	total: 451ms	remaining: 736ms
190:	learn: 0.2321989	total: 453ms	remaining: 734ms
191:	learn: 0.2313995	total: 456ms	remaining: 731ms
192:	learn: 0.2303279	total: 458ms	remaining: 728ms
193:	learn: 0.2295013	total: 460ms	remaining: 726ms
194:	learn: 0.2284370	total: 463ms	remaining: 724ms
195:	learn: 0.2272394	total: 465ms	remaining: 721ms
196:	learn: 0.2263299	total: 467ms	remaining: 718ms
197:	learn: 0.2257166	total: 469ms	remaining: 716ms
198:	learn: 0.2248650	total: 472ms	remaining: 713ms
199:	learn: 0.2240268	total: 474ms	remaining: 711ms
200:	learn: 0.2231389	total: 476ms	remaining: 708ms
201:	learn: 0.2222905	total: 478ms	remaining: 706ms
202:	learn: 0.2213866	total: 481ms	remaining: 703ms
203:	learn: 0.2205393	total: 483ms	remaining: 701ms
204:	learn: 0.2195534	total: 485ms	remaining: 698ms
205:	learn: 0.2188636	total: 487ms	remaining: 696ms
206:	learn: 0.2182941	total: 490ms	remaining: 693ms
207:	learn: 0.2175234	total: 492ms	remaining: 691ms
208:	learn: 0.2166551	total: 494ms	remaining: 688ms
209:	learn: 0.2156628	total: 496ms	remaining: 685ms
210:	learn: 0.2147675	total: 499ms	remaining: 683ms
211:	learn: 0.2140341	total: 501ms	remaining: 681ms
212:	learn: 0.2130638	total: 503ms	remaining: 678ms
213:	learn: 0.2121789	total: 505ms	remaining: 676ms
214:	learn: 0.2113196	total: 508ms	remaining: 673ms
215:	learn: 0.2106137	total: 510ms	remaining: 671ms
216:	learn: 0.2102435	total: 512ms	remaining: 668ms
217:	learn: 0.2095740	total: 515ms	remaining: 666ms
218:	learn: 0.2084122	total: 517ms	remaining: 664ms
219:	learn: 0.2075379	total: 520ms	remaining: 662ms
220:	learn: 0.2067857	total: 522ms	remaining: 659ms
221:	learn: 0.2057510	total: 524ms	remaining: 657ms
222:	learn: 0.2049788	total: 527ms	remaining: 654ms
223:	learn: 0.2040010	total: 529ms	remaining: 652ms
224:	learn: 0.2028940	total: 531ms	remaining: 649ms
225:	learn: 0.2020622	total: 534ms	remaining: 647ms
226:	learn: 0.2011427	total: 536ms	remaining: 645ms
227:	learn: 0.2006048	total: 538ms	remaining: 642ms
228:	learn: 0.1998993	total: 543ms	remaining: 643ms
229:	learn: 0.1990300	total: 546ms	remaining: 640ms
230:	learn: 0.1981033	total: 548ms	remaining: 638ms
231:	learn: 0.1973466	total: 550ms	remaining: 635ms
232:	learn: 0.1963239	total: 552ms	remaining: 633ms
233:	learn: 0.1956426	total: 555ms	remaining: 630ms
234:	learn: 0.1946437	total: 557ms	remaining: 628ms
235:	learn: 0.1938893	total: 559ms	remaining: 626ms
236:	learn: 0.1932918	total: 561ms	remaining: 623ms
237:	learn: 0.1927284	total: 564ms	remaining: 621ms
238:	learn: 0.1920843	total: 566ms	remaining: 618ms
239:	learn: 0.1914390	total: 569ms	remaining: 616ms
240:	learn: 0.1906405	total: 572ms	remaining: 614ms
241:	learn: 0.1900640	total: 574ms	remaining: 612ms
242:	learn: 0.1892296	total: 576ms	remaining: 610ms
243:	learn: 0.1882669	total: 579ms	remaining: 607ms
244:	learn: 0.1873879	total: 581ms	remaining: 605ms
245:	learn: 0.1867466	total: 583ms	remaining: 602ms
246:	learn: 0.1860431	total: 586ms	remaining: 600ms
247:	learn: 0.1852191	total: 588ms	remaining: 597ms
248:	learn: 0.1845446	total: 590ms	remaining: 595ms
249:	learn: 0.1834020	total: 592ms	remaining: 592ms
250:	learn: 0.1825995	total: 595ms	remaining: 590ms
251:	learn: 0.1820852	total: 597ms	remaining: 588ms
252:	learn: 0.1812926	total: 599ms	remaining: 585ms
253:	learn: 0.1806447	total: 602ms	remaining: 583ms
254:	learn: 0.1799085	total: 604ms	remaining: 580ms
255:	learn: 0.1792038	total: 607ms	remaining: 578ms
256:	learn: 0.1786016	total: 609ms	remaining: 576ms
257:	learn: 0.1780065	total: 611ms	remaining: 573ms
258:	learn: 0.1773282	total: 614ms	remaining: 571ms
259:	learn: 0.1767333	total: 616ms	remaining: 569ms
260:	learn: 0.1761574	total: 619ms	remaining: 567ms
261:	learn: 0.1755312	total: 621ms	remaining: 564ms
262:	learn: 0.1748717	total: 624ms	remaining: 562ms
263:	learn: 0.1741799	total: 626ms	remaining: 560ms
264:	learn: 0.1733825	total: 629ms	remaining: 558ms
265:	learn: 0.1727620	total: 631ms	remaining: 555ms
266:	learn: 0.1719393	total: 634ms	remaining: 553ms
267:	learn: 0.1712526	total: 636ms	remaining: 551ms
268:	learn: 0.1707232	total: 640ms	remaining: 550ms
269:	learn: 0.1698051	total: 643ms	remaining: 548ms
270:	learn: 0.1691197	total: 645ms	remaining: 545ms
271:	learn: 0.1686130	total: 648ms	remaining: 543ms
272:	learn: 0.1681484	total: 650ms	remaining: 541ms
273:	learn: 0.1675398	total: 653ms	remaining: 538ms
274:	learn: 0.1669642	total: 655ms	remaining: 536ms
275:	learn: 0.1661735	total: 658ms	remaining: 534ms
276:	learn: 0.1655495	total: 660ms	remaining: 531ms
277:	learn: 0.1650556	total: 662ms	remaining: 529ms
278:	learn: 0.1645026	total: 664ms	remaining: 526ms
279:	learn: 0.1638293	total: 667ms	remaining: 524ms
280:	learn: 0.1631330	total: 669ms	remaining: 521ms
281:	learn: 0.1624074	total: 671ms	remaining: 519ms
282:	learn: 0.1617931	total: 673ms	remaining: 516ms
283:	learn: 0.1613417	total: 676ms	remaining: 514ms
284:	learn: 0.1606346	total: 678ms	remaining: 512ms
285:	learn: 0.1600393	total: 680ms	remaining: 509ms
286:	learn: 0.1594270	total: 683ms	remaining: 507ms
287:	learn: 0.1586614	total: 685ms	remaining: 504ms
288:	learn: 0.1579281	total: 687ms	remaining: 502ms
289:	learn: 0.1573725	total: 689ms	remaining: 499ms
290:	learn: 0.1568409	total: 692ms	remaining: 497ms
291:	learn: 0.1561597	total: 694ms	remaining: 494ms
292:	learn: 0.1554431	total: 696ms	remaining: 492ms
293:	learn: 0.1549395	total: 699ms	remaining: 490ms
294:	learn: 0.1543320	total: 701ms	remaining: 487ms
295:	learn: 0.1538829	total: 703ms	remaining: 485ms
296:	learn: 0.1531872	total: 705ms	remaining: 482ms
297:	learn: 0.1526274	total: 708ms	remaining: 480ms
298:	learn: 0.1523221	total: 710ms	remaining: 477ms
299:	learn: 0.1517371	total: 713ms	remaining: 475ms
300:	learn: 0.1512891	total: 715ms	remaining: 473ms
301:	learn: 0.1507103	total: 717ms	remaining: 470ms
302:	learn: 0.1502394	total: 719ms	remaining: 468ms
303:	learn: 0.1495591	total: 722ms	remaining: 465ms
304:	learn: 0.1491118	total: 724ms	remaining: 463ms
305:	learn: 0.1483549	total: 726ms	remaining: 460ms
306:	learn: 0.1480394	total: 729ms	remaining: 458ms
307:	learn: 0.1473239	total: 731ms	remaining: 456ms
308:	learn: 0.1468071	total: 733ms	remaining: 453ms
309:	learn: 0.1463110	total: 737ms	remaining: 452ms
310:	learn: 0.1456556	total: 739ms	remaining: 449ms
311:	learn: 0.1450587	total: 742ms	remaining: 447ms
312:	learn: 0.1445312	total: 744ms	remaining: 444ms
313:	learn: 0.1441688	total: 746ms	remaining: 442ms
314:	learn: 0.1435106	total: 748ms	remaining: 439ms
315:	learn: 0.1427523	total: 751ms	remaining: 437ms
316:	learn: 0.1422563	total: 753ms	remaining: 435ms
317:	learn: 0.1417657	total: 755ms	remaining: 432ms
318:	learn: 0.1413410	total: 758ms	remaining: 430ms
319:	learn: 0.1410854	total: 760ms	remaining: 428ms
320:	learn: 0.1406948	total: 762ms	remaining: 425ms
321:	learn: 0.1401983	total: 764ms	remaining: 423ms
322:	learn: 0.1396835	total: 767ms	remaining: 420ms
323:	learn: 0.1391916	total: 769ms	remaining: 418ms
324:	learn: 0.1387641	total: 771ms	remaining: 415ms
325:	learn: 0.1383876	total: 774ms	remaining: 413ms
326:	learn: 0.1378716	total: 776ms	remaining: 411ms
327:	learn: 0.1373217	total: 778ms	remaining: 408ms
328:	learn: 0.1368519	total: 780ms	remaining: 406ms
329:	learn: 0.1364872	total: 783ms	remaining: 403ms
330:	learn: 0.1358959	total: 785ms	remaining: 401ms
331:	learn: 0.1353925	total: 787ms	remaining: 398ms
332:	learn: 0.1347982	total: 790ms	remaining: 396ms
333:	learn: 0.1345270	total: 792ms	remaining: 394ms
334:	learn: 0.1338884	total: 794ms	remaining: 391ms
335:	learn: 0.1333072	total: 797ms	remaining: 389ms
336:	learn: 0.1325900	total: 799ms	remaining: 386ms
337:	learn: 0.1321834	total: 801ms	remaining: 384ms
338:	learn: 0.1318139	total: 803ms	remaining: 382ms
339:	learn: 0.1312826	total: 806ms	remaining: 379ms
340:	learn: 0.1309283	total: 808ms	remaining: 377ms
341:	learn: 0.1304354	total: 810ms	remaining: 374ms
342:	learn: 0.1300019	total: 812ms	remaining: 372ms
343:	learn: 0.1296892	total: 815ms	remaining: 369ms
344:	learn: 0.1292672	total: 817ms	remaining: 367ms
345:	learn: 0.1288441	total: 819ms	remaining: 365ms
346:	learn: 0.1284202	total: 821ms	remaining: 362ms
347:	learn: 0.1279484	total: 824ms	remaining: 360ms
348:	learn: 0.1274566	total: 826ms	remaining: 357ms
349:	learn: 0.1269655	total: 828ms	remaining: 355ms
350:	learn: 0.1266929	total: 830ms	remaining: 352ms
351:	learn: 0.1262798	total: 835ms	remaining: 351ms
352:	learn: 0.1255939	total: 837ms	remaining: 349ms
353:	learn: 0.1251048	total: 839ms	remaining: 346ms
354:	learn: 0.1247007	total: 842ms	remaining: 344ms
355:	learn: 0.1243992	total: 844ms	remaining: 341ms
356:	learn: 0.1240172	total: 846ms	remaining: 339ms
357:	learn: 0.1235282	total: 848ms	remaining: 336ms
358:	learn: 0.1232314	total: 851ms	remaining: 334ms
359:	learn: 0.1228734	total: 853ms	remaining: 332ms
360:	learn: 0.1223691	total: 855ms	remaining: 329ms
361:	learn: 0.1220155	total: 858ms	remaining: 327ms
362:	learn: 0.1214939	total: 860ms	remaining: 325ms
363:	learn: 0.1210399	total: 863ms	remaining: 322ms
364:	learn: 0.1206881	total: 866ms	remaining: 320ms
365:	learn: 0.1202152	total: 868ms	remaining: 318ms
366:	learn: 0.1196979	total: 871ms	remaining: 316ms
367:	learn: 0.1193685	total: 874ms	remaining: 313ms
368:	learn: 0.1188689	total: 876ms	remaining: 311ms
369:	learn: 0.1185716	total: 878ms	remaining: 309ms
370:	learn: 0.1181243	total: 880ms	remaining: 306ms
371:	learn: 0.1177131	total: 883ms	remaining: 304ms
372:	learn: 0.1171809	total: 885ms	remaining: 301ms
373:	learn: 0.1166864	total: 887ms	remaining: 299ms
374:	learn: 0.1163448	total: 890ms	remaining: 297ms
375:	learn: 0.1158300	total: 892ms	remaining: 294ms
376:	learn: 0.1153364	total: 894ms	remaining: 292ms
377:	learn: 0.1149019	total: 896ms	remaining: 289ms
378:	learn: 0.1145395	total: 899ms	remaining: 287ms
379:	learn: 0.1140462	total: 901ms	remaining: 284ms
380:	learn: 0.1137536	total: 903ms	remaining: 282ms
381:	learn: 0.1134100	total: 906ms	remaining: 280ms
382:	learn: 0.1130484	total: 908ms	remaining: 277ms
383:	learn: 0.1125571	total: 911ms	remaining: 275ms
384:	learn: 0.1121974	total: 913ms	remaining: 273ms
385:	learn: 0.1117224	total: 915ms	remaining: 270ms
386:	learn: 0.1114912	total: 917ms	remaining: 268ms
387:	learn: 0.1111863	total: 920ms	remaining: 265ms
388:	learn: 0.1107888	total: 922ms	remaining: 263ms
389:	learn: 0.1103318	total: 924ms	remaining: 261ms
390:	learn: 0.1098515	total: 931ms	remaining: 260ms
391:	learn: 0.1094493	total: 934ms	remaining: 257ms
392:	learn: 0.1091565	total: 936ms	remaining: 255ms
393:	learn: 0.1087845	total: 938ms	remaining: 252ms
394:	learn: 0.1084250	total: 940ms	remaining: 250ms
395:	learn: 0.1080625	total: 943ms	remaining: 248ms
396:	learn: 0.1076871	total: 945ms	remaining: 245ms
397:	learn: 0.1072961	total: 947ms	remaining: 243ms
398:	learn: 0.1069108	total: 950ms	remaining: 240ms
399:	learn: 0.1065617	total: 952ms	remaining: 238ms
400:	learn: 0.1061837	total: 954ms	remaining: 236ms
401:	learn: 0.1058797	total: 957ms	remaining: 233ms
402:	learn: 0.1054446	total: 959ms	remaining: 231ms
403:	learn: 0.1050698	total: 961ms	remaining: 228ms
404:	learn: 0.1048033	total: 963ms	remaining: 226ms
405:	learn: 0.1044053	total: 965ms	remaining: 224ms
406:	learn: 0.1040302	total: 968ms	remaining: 221ms
407:	learn: 0.1037326	total: 970ms	remaining: 219ms
408:	learn: 0.1033871	total: 972ms	remaining: 216ms
409:	learn: 0.1030997	total: 975ms	remaining: 214ms
410:	learn: 0.1027496	total: 977ms	remaining: 212ms
411:	learn: 0.1023102	total: 979ms	remaining: 209ms
412:	learn: 0.1019111	total: 981ms	remaining: 207ms
413:	learn: 0.1016286	total: 984ms	remaining: 204ms
414:	learn: 0.1013652	total: 986ms	remaining: 202ms
415:	learn: 0.1010402	total: 988ms	remaining: 200ms
416:	learn: 0.1006589	total: 990ms	remaining: 197ms
417:	learn: 0.1003788	total: 993ms	remaining: 195ms
418:	learn: 0.1001087	total: 995ms	remaining: 192ms
419:	learn: 0.0997701	total: 997ms	remaining: 190ms
420:	learn: 0.0995213	total: 999ms	remaining: 188ms
421:	learn: 0.0992803	total: 1s	remaining: 185ms
422:	learn: 0.0989575	total: 1s	remaining: 183ms
423:	learn: 0.0986798	total: 1.01s	remaining: 180ms
424:	learn: 0.0984047	total: 1.01s	remaining: 178ms
425:	learn: 0.0981221	total: 1.01s	remaining: 176ms
426:	learn: 0.0977776	total: 1.01s	remaining: 173ms
427:	learn: 0.0973040	total: 1.01s	remaining: 171ms
428:	learn: 0.0970323	total: 1.02s	remaining: 168ms
429:	learn: 0.0967059	total: 1.02s	remaining: 166ms
430:	learn: 0.0964207	total: 1.02s	remaining: 164ms
431:	learn: 0.0960619	total: 1.02s	remaining: 161ms
432:	learn: 0.0957526	total: 1.03s	remaining: 159ms
433:	learn: 0.0954341	total: 1.03s	remaining: 157ms
434:	learn: 0.0950644	total: 1.03s	remaining: 154ms
435:	learn: 0.0947886	total: 1.03s	remaining: 152ms
436:	learn: 0.0944515	total: 1.04s	remaining: 149ms
437:	learn: 0.0941585	total: 1.04s	remaining: 147ms
438:	learn: 0.0937896	total: 1.04s	remaining: 145ms
439:	learn: 0.0935078	total: 1.04s	remaining: 142ms
440:	learn: 0.0931763	total: 1.05s	remaining: 140ms
441:	learn: 0.0928172	total: 1.05s	remaining: 138ms
442:	learn: 0.0925220	total: 1.05s	remaining: 135ms
443:	learn: 0.0921680	total: 1.05s	remaining: 133ms
444:	learn: 0.0919456	total: 1.05s	remaining: 130ms
445:	learn: 0.0916986	total: 1.06s	remaining: 128ms
446:	learn: 0.0913800	total: 1.06s	remaining: 126ms
447:	learn: 0.0910511	total: 1.06s	remaining: 123ms
448:	learn: 0.0907327	total: 1.06s	remaining: 121ms
449:	learn: 0.0904409	total: 1.07s	remaining: 118ms
450:	learn: 0.0899739	total: 1.07s	remaining: 116ms
451:	learn: 0.0896335	total: 1.07s	remaining: 114ms
452:	learn: 0.0893610	total: 1.07s	remaining: 112ms
453:	learn: 0.0891826	total: 1.08s	remaining: 109ms
454:	learn: 0.0889084	total: 1.08s	remaining: 107ms
455:	learn: 0.0886318	total: 1.08s	remaining: 104ms
456:	learn: 0.0883569	total: 1.08s	remaining: 102ms
457:	learn: 0.0880737	total: 1.09s	remaining: 99.6ms
458:	learn: 0.0878212	total: 1.09s	remaining: 97.2ms
459:	learn: 0.0875627	total: 1.09s	remaining: 94.9ms
460:	learn: 0.0872361	total: 1.09s	remaining: 92.5ms
461:	learn: 0.0869308	total: 1.09s	remaining: 90.1ms
462:	learn: 0.0866106	total: 1.1s	remaining: 87.7ms
463:	learn: 0.0863854	total: 1.1s	remaining: 85.4ms
464:	learn: 0.0861121	total: 1.1s	remaining: 83ms
465:	learn: 0.0857594	total: 1.1s	remaining: 80.6ms
466:	learn: 0.0853959	total: 1.11s	remaining: 78.3ms
467:	learn: 0.0850528	total: 1.11s	remaining: 76.3ms
468:	learn: 0.0847541	total: 1.12s	remaining: 73.9ms
469:	learn: 0.0845114	total: 1.12s	remaining: 71.5ms
470:	learn: 0.0842540	total: 1.12s	remaining: 69.2ms
471:	learn: 0.0838931	total: 1.13s	remaining: 66.8ms
472:	learn: 0.0836177	total: 1.13s	remaining: 64.4ms
473:	learn: 0.0834198	total: 1.13s	remaining: 62ms
474:	learn: 0.0832246	total: 1.13s	remaining: 59.6ms
475:	learn: 0.0829577	total: 1.14s	remaining: 57.3ms
476:	learn: 0.0826542	total: 1.14s	remaining: 54.9ms
477:	learn: 0.0824755	total: 1.14s	remaining: 52.5ms
478:	learn: 0.0821566	total: 1.14s	remaining: 50.1ms
479:	learn: 0.0819504	total: 1.15s	remaining: 47.7ms
480:	learn: 0.0817711	total: 1.15s	remaining: 45.3ms
481:	learn: 0.0814905	total: 1.15s	remaining: 43ms
482:	learn: 0.0812188	total: 1.15s	remaining: 40.6ms
483:	learn: 0.0808839	total: 1.16s	remaining: 38.2ms
484:	learn: 0.0806947	total: 1.16s	remaining: 35.8ms
485:	learn: 0.0803401	total: 1.16s	remaining: 33.4ms
486:	learn: 0.0800408	total: 1.16s	remaining: 31ms
487:	learn: 0.0797800	total: 1.16s	remaining: 28.6ms
488:	learn: 0.0794863	total: 1.17s	remaining: 26.3ms
489:	learn: 0.0792235	total: 1.17s	remaining: 23.9ms
490:	learn: 0.0789064	total: 1.17s	remaining: 21.5ms
491:	learn: 0.0786355	total: 1.17s	remaining: 19.1ms
492:	learn: 0.0783596	total: 1.18s	remaining: 16.7ms
493:	learn: 0.0780083	total: 1.18s	remaining: 14.3ms
494:	learn: 0.0778218	total: 1.18s	remaining: 11.9ms
495:	learn: 0.0775589	total: 1.18s	remaining: 9.55ms
496:	learn: 0.0773239	total: 1.19s	remaining: 7.16ms
497:	learn: 0.0771095	total: 1.19s	remaining: 4.77ms
498:	learn: 0.0768687	total: 1.19s	remaining: 2.39ms
499:	learn: 0.0765568	total: 1.19s	remaining: 0us
0:	learn: 0.6881737	total: 2.9ms	remaining: 1.45s
1:	learn: 0.6831239	total: 5.29ms	remaining: 1.32s
2:	learn: 0.6761026	total: 7.58ms	remaining: 1.25s
3:	learn: 0.6706294	total: 9.89ms	remaining: 1.23s
4:	learn: 0.6652409	total: 12.2ms	remaining: 1.21s
5:	learn: 0.6592031	total: 14.5ms	remaining: 1.19s
6:	learn: 0.6531412	total: 16.7ms	remaining: 1.18s
7:	learn: 0.6468683	total: 19.1ms	remaining: 1.17s
8:	learn: 0.6419916	total: 21.4ms	remaining: 1.17s
9:	learn: 0.6374706	total: 23.7ms	remaining: 1.16s
10:	learn: 0.6309301	total: 25.9ms	remaining: 1.15s
11:	learn: 0.6252295	total: 28.2ms	remaining: 1.15s
12:	learn: 0.6224626	total: 30.6ms	remaining: 1.15s
13:	learn: 0.6162793	total: 32.9ms	remaining: 1.14s
14:	learn: 0.6112890	total: 35.2ms	remaining: 1.14s
15:	learn: 0.6059406	total: 38.3ms	remaining: 1.16s
16:	learn: 0.6003249	total: 41.1ms	remaining: 1.17s
17:	learn: 0.5953382	total: 43.6ms	remaining: 1.17s
18:	learn: 0.5907735	total: 45.9ms	remaining: 1.16s
19:	learn: 0.5855856	total: 48.3ms	remaining: 1.16s
20:	learn: 0.5818512	total: 50.7ms	remaining: 1.16s
21:	learn: 0.5771095	total: 52.9ms	remaining: 1.15s
22:	learn: 0.5724869	total: 55.5ms	remaining: 1.15s
23:	learn: 0.5685018	total: 57.9ms	remaining: 1.15s
24:	learn: 0.5645150	total: 60.3ms	remaining: 1.15s
25:	learn: 0.5589715	total: 62.5ms	remaining: 1.14s
26:	learn: 0.5535508	total: 64.8ms	remaining: 1.13s
27:	learn: 0.5500159	total: 67ms	remaining: 1.13s
28:	learn: 0.5461227	total: 69.4ms	remaining: 1.13s
29:	learn: 0.5425255	total: 71.7ms	remaining: 1.12s
30:	learn: 0.5385662	total: 74ms	remaining: 1.12s
31:	learn: 0.5341064	total: 76.3ms	remaining: 1.12s
32:	learn: 0.5297349	total: 78.6ms	remaining: 1.11s
33:	learn: 0.5263909	total: 80.8ms	remaining: 1.11s
34:	learn: 0.5220501	total: 83.2ms	remaining: 1.1s
35:	learn: 0.5183265	total: 85.3ms	remaining: 1.1s
36:	learn: 0.5146864	total: 87.6ms	remaining: 1.09s
37:	learn: 0.5116564	total: 89.8ms	remaining: 1.09s
38:	learn: 0.5064947	total: 92.1ms	remaining: 1.09s
39:	learn: 0.5021784	total: 94.4ms	remaining: 1.08s
40:	learn: 0.4984464	total: 96.6ms	remaining: 1.08s
41:	learn: 0.4952256	total: 98.9ms	remaining: 1.08s
42:	learn: 0.4926358	total: 101ms	remaining: 1.08s
43:	learn: 0.4891020	total: 103ms	remaining: 1.07s
44:	learn: 0.4855969	total: 106ms	remaining: 1.07s
45:	learn: 0.4831926	total: 108ms	remaining: 1.06s
46:	learn: 0.4784326	total: 110ms	remaining: 1.06s
47:	learn: 0.4743805	total: 113ms	remaining: 1.06s
48:	learn: 0.4713713	total: 115ms	remaining: 1.06s
49:	learn: 0.4689443	total: 117ms	remaining: 1.05s
50:	learn: 0.4651958	total: 119ms	remaining: 1.05s
51:	learn: 0.4627791	total: 122ms	remaining: 1.05s
52:	learn: 0.4600085	total: 124ms	remaining: 1.04s
53:	learn: 0.4569907	total: 126ms	remaining: 1.04s
54:	learn: 0.4536093	total: 129ms	remaining: 1.04s
55:	learn: 0.4500670	total: 131ms	remaining: 1.04s
56:	learn: 0.4469934	total: 133ms	remaining: 1.03s
57:	learn: 0.4438394	total: 136ms	remaining: 1.03s
58:	learn: 0.4403000	total: 138ms	remaining: 1.03s
59:	learn: 0.4376555	total: 140ms	remaining: 1.03s
60:	learn: 0.4346896	total: 142ms	remaining: 1.02s
61:	learn: 0.4310668	total: 145ms	remaining: 1.02s
62:	learn: 0.4281496	total: 147ms	remaining: 1.02s
63:	learn: 0.4251988	total: 149ms	remaining: 1.02s
64:	learn: 0.4220293	total: 152ms	remaining: 1.01s
65:	learn: 0.4190286	total: 154ms	remaining: 1.01s
66:	learn: 0.4159964	total: 156ms	remaining: 1.01s
67:	learn: 0.4129507	total: 158ms	remaining: 1.01s
68:	learn: 0.4101669	total: 161ms	remaining: 1s
69:	learn: 0.4083375	total: 163ms	remaining: 1s
70:	learn: 0.4057286	total: 165ms	remaining: 997ms
71:	learn: 0.4029942	total: 167ms	remaining: 995ms
72:	learn: 0.4002515	total: 170ms	remaining: 992ms
73:	learn: 0.3976993	total: 173ms	remaining: 997ms
74:	learn: 0.3950173	total: 175ms	remaining: 994ms
75:	learn: 0.3917215	total: 178ms	remaining: 992ms
76:	learn: 0.3894997	total: 180ms	remaining: 989ms
77:	learn: 0.3871529	total: 182ms	remaining: 986ms
78:	learn: 0.3846109	total: 185ms	remaining: 984ms
79:	learn: 0.3820492	total: 187ms	remaining: 981ms
80:	learn: 0.3796646	total: 189ms	remaining: 978ms
81:	learn: 0.3765269	total: 191ms	remaining: 976ms
82:	learn: 0.3735579	total: 194ms	remaining: 974ms
83:	learn: 0.3713182	total: 196ms	remaining: 973ms
84:	learn: 0.3689165	total: 199ms	remaining: 971ms
85:	learn: 0.3663381	total: 201ms	remaining: 969ms
86:	learn: 0.3636061	total: 204ms	remaining: 966ms
87:	learn: 0.3616476	total: 206ms	remaining: 964ms
88:	learn: 0.3599451	total: 208ms	remaining: 961ms
89:	learn: 0.3570007	total: 210ms	remaining: 959ms
90:	learn: 0.3552359	total: 213ms	remaining: 956ms
91:	learn: 0.3522013	total: 215ms	remaining: 953ms
92:	learn: 0.3500545	total: 218ms	remaining: 954ms
93:	learn: 0.3484188	total: 220ms	remaining: 951ms
94:	learn: 0.3463551	total: 223ms	remaining: 949ms
95:	learn: 0.3446676	total: 225ms	remaining: 947ms
96:	learn: 0.3422545	total: 227ms	remaining: 945ms
97:	learn: 0.3402425	total: 230ms	remaining: 943ms
98:	learn: 0.3385337	total: 232ms	remaining: 941ms
99:	learn: 0.3359343	total: 235ms	remaining: 939ms
100:	learn: 0.3336376	total: 237ms	remaining: 937ms
101:	learn: 0.3325468	total: 240ms	remaining: 935ms
102:	learn: 0.3305442	total: 242ms	remaining: 933ms
103:	learn: 0.3285717	total: 245ms	remaining: 931ms
104:	learn: 0.3262115	total: 247ms	remaining: 929ms
105:	learn: 0.3245022	total: 250ms	remaining: 928ms
106:	learn: 0.3223387	total: 252ms	remaining: 927ms
107:	learn: 0.3202911	total: 255ms	remaining: 925ms
108:	learn: 0.3186621	total: 257ms	remaining: 923ms
109:	learn: 0.3171889	total: 260ms	remaining: 921ms
110:	learn: 0.3152481	total: 262ms	remaining: 918ms
111:	learn: 0.3127998	total: 265ms	remaining: 918ms
112:	learn: 0.3109109	total: 269ms	remaining: 922ms
113:	learn: 0.3082536	total: 272ms	remaining: 920ms
114:	learn: 0.3065387	total: 274ms	remaining: 918ms
115:	learn: 0.3050260	total: 276ms	remaining: 915ms
116:	learn: 0.3034109	total: 279ms	remaining: 913ms
117:	learn: 0.3019638	total: 281ms	remaining: 911ms
118:	learn: 0.3005761	total: 284ms	remaining: 908ms
119:	learn: 0.2986810	total: 286ms	remaining: 906ms
120:	learn: 0.2967956	total: 289ms	remaining: 904ms
121:	learn: 0.2954449	total: 291ms	remaining: 902ms
122:	learn: 0.2932306	total: 294ms	remaining: 900ms
123:	learn: 0.2920511	total: 296ms	remaining: 897ms
124:	learn: 0.2903032	total: 298ms	remaining: 895ms
125:	learn: 0.2884170	total: 301ms	remaining: 893ms
126:	learn: 0.2864910	total: 303ms	remaining: 891ms
127:	learn: 0.2848066	total: 306ms	remaining: 889ms
128:	learn: 0.2836757	total: 308ms	remaining: 886ms
129:	learn: 0.2822204	total: 311ms	remaining: 884ms
130:	learn: 0.2807250	total: 313ms	remaining: 882ms
131:	learn: 0.2789823	total: 315ms	remaining: 879ms
132:	learn: 0.2776076	total: 318ms	remaining: 877ms
133:	learn: 0.2762408	total: 321ms	remaining: 877ms
134:	learn: 0.2743953	total: 323ms	remaining: 875ms
135:	learn: 0.2734995	total: 326ms	remaining: 873ms
136:	learn: 0.2718947	total: 328ms	remaining: 870ms
137:	learn: 0.2700878	total: 331ms	remaining: 868ms
138:	learn: 0.2683712	total: 333ms	remaining: 865ms
139:	learn: 0.2668273	total: 336ms	remaining: 863ms
140:	learn: 0.2656352	total: 338ms	remaining: 861ms
141:	learn: 0.2642544	total: 340ms	remaining: 858ms
142:	learn: 0.2630965	total: 343ms	remaining: 856ms
143:	learn: 0.2618504	total: 345ms	remaining: 853ms
144:	learn: 0.2601061	total: 348ms	remaining: 851ms
145:	learn: 0.2587270	total: 350ms	remaining: 849ms
146:	learn: 0.2578838	total: 352ms	remaining: 846ms
147:	learn: 0.2562063	total: 355ms	remaining: 843ms
148:	learn: 0.2551956	total: 357ms	remaining: 841ms
149:	learn: 0.2537834	total: 359ms	remaining: 838ms
150:	learn: 0.2521559	total: 362ms	remaining: 836ms
151:	learn: 0.2507189	total: 366ms	remaining: 839ms
152:	learn: 0.2495592	total: 369ms	remaining: 837ms
153:	learn: 0.2485153	total: 371ms	remaining: 834ms
154:	learn: 0.2474057	total: 373ms	remaining: 831ms
155:	learn: 0.2462270	total: 376ms	remaining: 829ms
156:	learn: 0.2451368	total: 378ms	remaining: 826ms
157:	learn: 0.2436647	total: 380ms	remaining: 823ms
158:	learn: 0.2423893	total: 383ms	remaining: 821ms
159:	learn: 0.2413720	total: 385ms	remaining: 818ms
160:	learn: 0.2406006	total: 387ms	remaining: 815ms
161:	learn: 0.2395966	total: 390ms	remaining: 813ms
162:	learn: 0.2384893	total: 392ms	remaining: 810ms
163:	learn: 0.2373684	total: 394ms	remaining: 808ms
164:	learn: 0.2360214	total: 397ms	remaining: 805ms
165:	learn: 0.2351934	total: 399ms	remaining: 803ms
166:	learn: 0.2341769	total: 401ms	remaining: 800ms
167:	learn: 0.2328508	total: 404ms	remaining: 797ms
168:	learn: 0.2319457	total: 406ms	remaining: 795ms
169:	learn: 0.2307772	total: 408ms	remaining: 793ms
170:	learn: 0.2292597	total: 411ms	remaining: 790ms
171:	learn: 0.2281407	total: 413ms	remaining: 788ms
172:	learn: 0.2272865	total: 415ms	remaining: 785ms
173:	learn: 0.2260712	total: 418ms	remaining: 782ms
174:	learn: 0.2245775	total: 420ms	remaining: 780ms
175:	learn: 0.2233971	total: 422ms	remaining: 777ms
176:	learn: 0.2225225	total: 425ms	remaining: 775ms
177:	learn: 0.2213167	total: 427ms	remaining: 772ms
178:	learn: 0.2201504	total: 429ms	remaining: 770ms
179:	learn: 0.2190593	total: 432ms	remaining: 767ms
180:	learn: 0.2178329	total: 434ms	remaining: 765ms
181:	learn: 0.2166884	total: 436ms	remaining: 762ms
182:	learn: 0.2157891	total: 439ms	remaining: 760ms
183:	learn: 0.2149296	total: 441ms	remaining: 757ms
184:	learn: 0.2142096	total: 444ms	remaining: 755ms
185:	learn: 0.2133818	total: 447ms	remaining: 754ms
186:	learn: 0.2123564	total: 449ms	remaining: 752ms
187:	learn: 0.2111822	total: 451ms	remaining: 749ms
188:	learn: 0.2098321	total: 454ms	remaining: 747ms
189:	learn: 0.2091195	total: 456ms	remaining: 744ms
190:	learn: 0.2078318	total: 463ms	remaining: 749ms
191:	learn: 0.2071328	total: 466ms	remaining: 747ms
192:	learn: 0.2059449	total: 468ms	remaining: 744ms
193:	learn: 0.2050105	total: 470ms	remaining: 742ms
194:	learn: 0.2039568	total: 473ms	remaining: 739ms
195:	learn: 0.2032354	total: 475ms	remaining: 736ms
196:	learn: 0.2024126	total: 477ms	remaining: 734ms
197:	learn: 0.2016898	total: 480ms	remaining: 732ms
198:	learn: 0.2006059	total: 482ms	remaining: 729ms
199:	learn: 0.1998019	total: 485ms	remaining: 727ms
200:	learn: 0.1987887	total: 487ms	remaining: 724ms
201:	learn: 0.1981618	total: 489ms	remaining: 722ms
202:	learn: 0.1973406	total: 492ms	remaining: 719ms
203:	learn: 0.1966388	total: 494ms	remaining: 717ms
204:	learn: 0.1958015	total: 496ms	remaining: 714ms
205:	learn: 0.1952057	total: 498ms	remaining: 711ms
206:	learn: 0.1942198	total: 501ms	remaining: 709ms
207:	learn: 0.1929329	total: 503ms	remaining: 706ms
208:	learn: 0.1921276	total: 505ms	remaining: 703ms
209:	learn: 0.1912825	total: 507ms	remaining: 701ms
210:	learn: 0.1903982	total: 510ms	remaining: 698ms
211:	learn: 0.1896203	total: 512ms	remaining: 695ms
212:	learn: 0.1886170	total: 514ms	remaining: 693ms
213:	learn: 0.1875572	total: 517ms	remaining: 691ms
214:	learn: 0.1869648	total: 519ms	remaining: 688ms
215:	learn: 0.1864464	total: 522ms	remaining: 686ms
216:	learn: 0.1857859	total: 524ms	remaining: 683ms
217:	learn: 0.1849940	total: 527ms	remaining: 681ms
218:	learn: 0.1842690	total: 529ms	remaining: 679ms
219:	learn: 0.1837064	total: 532ms	remaining: 677ms
220:	learn: 0.1830120	total: 534ms	remaining: 674ms
221:	learn: 0.1823462	total: 536ms	remaining: 672ms
222:	learn: 0.1813558	total: 539ms	remaining: 670ms
223:	learn: 0.1804593	total: 541ms	remaining: 667ms
224:	learn: 0.1795490	total: 544ms	remaining: 665ms
225:	learn: 0.1788749	total: 548ms	remaining: 664ms
226:	learn: 0.1781757	total: 550ms	remaining: 662ms
227:	learn: 0.1774400	total: 553ms	remaining: 660ms
228:	learn: 0.1767934	total: 559ms	remaining: 662ms
229:	learn: 0.1761277	total: 562ms	remaining: 659ms
230:	learn: 0.1756168	total: 564ms	remaining: 657ms
231:	learn: 0.1748689	total: 566ms	remaining: 654ms
232:	learn: 0.1740093	total: 569ms	remaining: 652ms
233:	learn: 0.1730357	total: 571ms	remaining: 649ms
234:	learn: 0.1721723	total: 574ms	remaining: 647ms
235:	learn: 0.1714669	total: 576ms	remaining: 645ms
236:	learn: 0.1707832	total: 579ms	remaining: 642ms
237:	learn: 0.1701224	total: 581ms	remaining: 640ms
238:	learn: 0.1694247	total: 583ms	remaining: 637ms
239:	learn: 0.1688444	total: 586ms	remaining: 635ms
240:	learn: 0.1681446	total: 588ms	remaining: 632ms
241:	learn: 0.1674284	total: 591ms	remaining: 630ms
242:	learn: 0.1667833	total: 593ms	remaining: 627ms
243:	learn: 0.1660281	total: 596ms	remaining: 625ms
244:	learn: 0.1653531	total: 598ms	remaining: 622ms
245:	learn: 0.1647192	total: 600ms	remaining: 620ms
246:	learn: 0.1638514	total: 603ms	remaining: 618ms
247:	learn: 0.1632461	total: 606ms	remaining: 616ms
248:	learn: 0.1626850	total: 609ms	remaining: 614ms
249:	learn: 0.1619342	total: 612ms	remaining: 612ms
250:	learn: 0.1613675	total: 614ms	remaining: 609ms
251:	learn: 0.1607158	total: 617ms	remaining: 607ms
252:	learn: 0.1601232	total: 619ms	remaining: 604ms
253:	learn: 0.1596665	total: 622ms	remaining: 602ms
254:	learn: 0.1589817	total: 624ms	remaining: 600ms
255:	learn: 0.1584542	total: 627ms	remaining: 597ms
256:	learn: 0.1578364	total: 629ms	remaining: 595ms
257:	learn: 0.1572299	total: 632ms	remaining: 592ms
258:	learn: 0.1566204	total: 634ms	remaining: 590ms
259:	learn: 0.1561116	total: 637ms	remaining: 588ms
260:	learn: 0.1556314	total: 656ms	remaining: 601ms
261:	learn: 0.1550578	total: 660ms	remaining: 599ms
262:	learn: 0.1544624	total: 662ms	remaining: 597ms
263:	learn: 0.1535589	total: 665ms	remaining: 594ms
264:	learn: 0.1528475	total: 667ms	remaining: 592ms
265:	learn: 0.1523410	total: 669ms	remaining: 589ms
266:	learn: 0.1517967	total: 672ms	remaining: 586ms
267:	learn: 0.1511868	total: 674ms	remaining: 584ms
268:	learn: 0.1506293	total: 677ms	remaining: 581ms
269:	learn: 0.1497745	total: 679ms	remaining: 579ms
270:	learn: 0.1491657	total: 682ms	remaining: 576ms
271:	learn: 0.1486088	total: 685ms	remaining: 574ms
272:	learn: 0.1478330	total: 687ms	remaining: 571ms
273:	learn: 0.1471146	total: 690ms	remaining: 569ms
274:	learn: 0.1465163	total: 692ms	remaining: 566ms
275:	learn: 0.1455485	total: 694ms	remaining: 563ms
276:	learn: 0.1450531	total: 697ms	remaining: 561ms
277:	learn: 0.1444902	total: 699ms	remaining: 558ms
278:	learn: 0.1439239	total: 701ms	remaining: 556ms
279:	learn: 0.1435770	total: 704ms	remaining: 553ms
280:	learn: 0.1429054	total: 706ms	remaining: 550ms
281:	learn: 0.1422676	total: 708ms	remaining: 548ms
282:	learn: 0.1416287	total: 711ms	remaining: 545ms
283:	learn: 0.1410820	total: 713ms	remaining: 542ms
284:	learn: 0.1405279	total: 715ms	remaining: 540ms
285:	learn: 0.1400398	total: 718ms	remaining: 537ms
286:	learn: 0.1395404	total: 720ms	remaining: 534ms
287:	learn: 0.1387547	total: 722ms	remaining: 532ms
288:	learn: 0.1382485	total: 725ms	remaining: 529ms
289:	learn: 0.1377566	total: 727ms	remaining: 526ms
290:	learn: 0.1373297	total: 729ms	remaining: 524ms
291:	learn: 0.1367923	total: 732ms	remaining: 521ms
292:	learn: 0.1361446	total: 734ms	remaining: 519ms
293:	learn: 0.1355172	total: 753ms	remaining: 528ms
294:	learn: 0.1348852	total: 755ms	remaining: 525ms
295:	learn: 0.1345799	total: 759ms	remaining: 523ms
296:	learn: 0.1340880	total: 761ms	remaining: 520ms
297:	learn: 0.1337142	total: 764ms	remaining: 518ms
298:	learn: 0.1333542	total: 766ms	remaining: 515ms
299:	learn: 0.1329554	total: 768ms	remaining: 512ms
300:	learn: 0.1324151	total: 771ms	remaining: 510ms
301:	learn: 0.1319408	total: 773ms	remaining: 507ms
302:	learn: 0.1311688	total: 775ms	remaining: 504ms
303:	learn: 0.1307755	total: 778ms	remaining: 501ms
304:	learn: 0.1302451	total: 780ms	remaining: 499ms
305:	learn: 0.1296191	total: 782ms	remaining: 496ms
306:	learn: 0.1289901	total: 785ms	remaining: 493ms
307:	learn: 0.1284633	total: 787ms	remaining: 491ms
308:	learn: 0.1279194	total: 789ms	remaining: 488ms
309:	learn: 0.1272975	total: 792ms	remaining: 485ms
310:	learn: 0.1266872	total: 794ms	remaining: 482ms
311:	learn: 0.1263308	total: 796ms	remaining: 480ms
312:	learn: 0.1257008	total: 798ms	remaining: 477ms
313:	learn: 0.1252720	total: 801ms	remaining: 474ms
314:	learn: 0.1247967	total: 803ms	remaining: 472ms
315:	learn: 0.1243449	total: 805ms	remaining: 469ms
316:	learn: 0.1238424	total: 808ms	remaining: 466ms
317:	learn: 0.1233417	total: 810ms	remaining: 464ms
318:	learn: 0.1228793	total: 812ms	remaining: 461ms
319:	learn: 0.1222674	total: 815ms	remaining: 458ms
320:	learn: 0.1215653	total: 817ms	remaining: 456ms
321:	learn: 0.1211018	total: 819ms	remaining: 453ms
322:	learn: 0.1205379	total: 822ms	remaining: 450ms
323:	learn: 0.1200298	total: 824ms	remaining: 448ms
324:	learn: 0.1194069	total: 826ms	remaining: 445ms
325:	learn: 0.1188812	total: 829ms	remaining: 442ms
326:	learn: 0.1184083	total: 831ms	remaining: 440ms
327:	learn: 0.1181316	total: 833ms	remaining: 437ms
328:	learn: 0.1176612	total: 850ms	remaining: 442ms
329:	learn: 0.1170615	total: 853ms	remaining: 439ms
330:	learn: 0.1165440	total: 856ms	remaining: 437ms
331:	learn: 0.1160792	total: 858ms	remaining: 434ms
332:	learn: 0.1155917	total: 860ms	remaining: 431ms
333:	learn: 0.1153120	total: 862ms	remaining: 429ms
334:	learn: 0.1148332	total: 865ms	remaining: 426ms
335:	learn: 0.1143841	total: 867ms	remaining: 423ms
336:	learn: 0.1137905	total: 869ms	remaining: 420ms
337:	learn: 0.1133237	total: 872ms	remaining: 418ms
338:	learn: 0.1129757	total: 874ms	remaining: 415ms
339:	learn: 0.1126173	total: 876ms	remaining: 412ms
340:	learn: 0.1122386	total: 879ms	remaining: 410ms
341:	learn: 0.1118293	total: 881ms	remaining: 407ms
342:	learn: 0.1114587	total: 883ms	remaining: 404ms
343:	learn: 0.1110090	total: 886ms	remaining: 402ms
344:	learn: 0.1105956	total: 888ms	remaining: 399ms
345:	learn: 0.1101220	total: 890ms	remaining: 396ms
346:	learn: 0.1098138	total: 893ms	remaining: 394ms
347:	learn: 0.1093782	total: 895ms	remaining: 391ms
348:	learn: 0.1088812	total: 897ms	remaining: 388ms
349:	learn: 0.1084566	total: 900ms	remaining: 386ms
350:	learn: 0.1081912	total: 902ms	remaining: 383ms
351:	learn: 0.1077310	total: 904ms	remaining: 380ms
352:	learn: 0.1072117	total: 906ms	remaining: 377ms
353:	learn: 0.1068434	total: 909ms	remaining: 375ms
354:	learn: 0.1064500	total: 911ms	remaining: 372ms
355:	learn: 0.1061827	total: 913ms	remaining: 369ms
356:	learn: 0.1057804	total: 916ms	remaining: 367ms
357:	learn: 0.1054324	total: 918ms	remaining: 364ms
358:	learn: 0.1051391	total: 920ms	remaining: 361ms
359:	learn: 0.1047462	total: 922ms	remaining: 359ms
360:	learn: 0.1043393	total: 925ms	remaining: 356ms
361:	learn: 0.1038246	total: 927ms	remaining: 353ms
362:	learn: 0.1034454	total: 929ms	remaining: 351ms
363:	learn: 0.1030710	total: 932ms	remaining: 348ms
364:	learn: 0.1028329	total: 934ms	remaining: 345ms
365:	learn: 0.1025565	total: 937ms	remaining: 343ms
366:	learn: 0.1022071	total: 948ms	remaining: 343ms
367:	learn: 0.1017479	total: 950ms	remaining: 341ms
368:	learn: 0.1012745	total: 952ms	remaining: 338ms
369:	learn: 0.1009779	total: 955ms	remaining: 335ms
370:	learn: 0.1006576	total: 957ms	remaining: 333ms
371:	learn: 0.1004483	total: 959ms	remaining: 330ms
372:	learn: 0.0999925	total: 964ms	remaining: 328ms
373:	learn: 0.0995211	total: 966ms	remaining: 326ms
374:	learn: 0.0991882	total: 969ms	remaining: 323ms
375:	learn: 0.0989219	total: 971ms	remaining: 320ms
376:	learn: 0.0985643	total: 974ms	remaining: 318ms
377:	learn: 0.0981997	total: 976ms	remaining: 315ms
378:	learn: 0.0978770	total: 978ms	remaining: 312ms
379:	learn: 0.0975332	total: 981ms	remaining: 310ms
380:	learn: 0.0970067	total: 983ms	remaining: 307ms
381:	learn: 0.0967376	total: 985ms	remaining: 304ms
382:	learn: 0.0963983	total: 988ms	remaining: 302ms
383:	learn: 0.0960689	total: 990ms	remaining: 299ms
384:	learn: 0.0956203	total: 993ms	remaining: 296ms
385:	learn: 0.0953008	total: 995ms	remaining: 294ms
386:	learn: 0.0949204	total: 997ms	remaining: 291ms
387:	learn: 0.0945654	total: 999ms	remaining: 288ms
388:	learn: 0.0941376	total: 1s	remaining: 286ms
389:	learn: 0.0936736	total: 1s	remaining: 283ms
390:	learn: 0.0933182	total: 1.01s	remaining: 280ms
391:	learn: 0.0930118	total: 1.01s	remaining: 278ms
392:	learn: 0.0927149	total: 1.01s	remaining: 275ms
393:	learn: 0.0924064	total: 1.01s	remaining: 273ms
394:	learn: 0.0920648	total: 1.01s	remaining: 270ms
395:	learn: 0.0917547	total: 1.02s	remaining: 267ms
396:	learn: 0.0914389	total: 1.02s	remaining: 265ms
397:	learn: 0.0911489	total: 1.02s	remaining: 262ms
398:	learn: 0.0908009	total: 1.02s	remaining: 259ms
399:	learn: 0.0905042	total: 1.03s	remaining: 257ms
400:	learn: 0.0901597	total: 1.03s	remaining: 254ms
401:	learn: 0.0899246	total: 1.03s	remaining: 251ms
402:	learn: 0.0895446	total: 1.03s	remaining: 249ms
403:	learn: 0.0893130	total: 1.03s	remaining: 246ms
404:	learn: 0.0890581	total: 1.04s	remaining: 244ms
405:	learn: 0.0888010	total: 1.04s	remaining: 241ms
406:	learn: 0.0885081	total: 1.04s	remaining: 239ms
407:	learn: 0.0881975	total: 1.05s	remaining: 236ms
408:	learn: 0.0879131	total: 1.05s	remaining: 233ms
409:	learn: 0.0875693	total: 1.05s	remaining: 231ms
410:	learn: 0.0872846	total: 1.05s	remaining: 228ms
411:	learn: 0.0869831	total: 1.06s	remaining: 226ms
412:	learn: 0.0866870	total: 1.06s	remaining: 223ms
413:	learn: 0.0863084	total: 1.06s	remaining: 220ms
414:	learn: 0.0860951	total: 1.06s	remaining: 218ms
415:	learn: 0.0858204	total: 1.06s	remaining: 215ms
416:	learn: 0.0856102	total: 1.07s	remaining: 212ms
417:	learn: 0.0853090	total: 1.07s	remaining: 210ms
418:	learn: 0.0849064	total: 1.07s	remaining: 207ms
419:	learn: 0.0846055	total: 1.07s	remaining: 205ms
420:	learn: 0.0844800	total: 1.08s	remaining: 202ms
421:	learn: 0.0842301	total: 1.08s	remaining: 199ms
422:	learn: 0.0839332	total: 1.08s	remaining: 197ms
423:	learn: 0.0837419	total: 1.08s	remaining: 194ms
424:	learn: 0.0834981	total: 1.08s	remaining: 192ms
425:	learn: 0.0831606	total: 1.09s	remaining: 189ms
426:	learn: 0.0828045	total: 1.09s	remaining: 186ms
427:	learn: 0.0825360	total: 1.09s	remaining: 184ms
428:	learn: 0.0822888	total: 1.09s	remaining: 181ms
429:	learn: 0.0819238	total: 1.1s	remaining: 179ms
430:	learn: 0.0816875	total: 1.1s	remaining: 176ms
431:	learn: 0.0813725	total: 1.1s	remaining: 173ms
432:	learn: 0.0811340	total: 1.1s	remaining: 171ms
433:	learn: 0.0809119	total: 1.11s	remaining: 168ms
434:	learn: 0.0805797	total: 1.11s	remaining: 166ms
435:	learn: 0.0802288	total: 1.11s	remaining: 163ms
436:	learn: 0.0799904	total: 1.11s	remaining: 161ms
437:	learn: 0.0797829	total: 1.11s	remaining: 158ms
438:	learn: 0.0794984	total: 1.12s	remaining: 155ms
439:	learn: 0.0793069	total: 1.12s	remaining: 153ms
440:	learn: 0.0789187	total: 1.12s	remaining: 150ms
441:	learn: 0.0787047	total: 1.13s	remaining: 148ms
442:	learn: 0.0783506	total: 1.13s	remaining: 145ms
443:	learn: 0.0780253	total: 1.13s	remaining: 143ms
444:	learn: 0.0777832	total: 1.13s	remaining: 140ms
445:	learn: 0.0775047	total: 1.13s	remaining: 137ms
446:	learn: 0.0772309	total: 1.14s	remaining: 135ms
447:	learn: 0.0769078	total: 1.14s	remaining: 132ms
448:	learn: 0.0766214	total: 1.14s	remaining: 130ms
449:	learn: 0.0764199	total: 1.15s	remaining: 127ms
450:	learn: 0.0761946	total: 1.15s	remaining: 125ms
451:	learn: 0.0759973	total: 1.15s	remaining: 122ms
452:	learn: 0.0757308	total: 1.16s	remaining: 120ms
453:	learn: 0.0755033	total: 1.16s	remaining: 118ms
454:	learn: 0.0751759	total: 1.16s	remaining: 115ms
455:	learn: 0.0749828	total: 1.17s	remaining: 112ms
456:	learn: 0.0747209	total: 1.17s	remaining: 110ms
457:	learn: 0.0744755	total: 1.17s	remaining: 107ms
458:	learn: 0.0742540	total: 1.17s	remaining: 105ms
459:	learn: 0.0740387	total: 1.18s	remaining: 102ms
460:	learn: 0.0738311	total: 1.18s	remaining: 99.7ms
461:	learn: 0.0735700	total: 1.18s	remaining: 97.1ms
462:	learn: 0.0732886	total: 1.18s	remaining: 94.5ms
463:	learn: 0.0731035	total: 1.19s	remaining: 92ms
464:	learn: 0.0727865	total: 1.19s	remaining: 89.4ms
465:	learn: 0.0726291	total: 1.19s	remaining: 86.9ms
466:	learn: 0.0724102	total: 1.19s	remaining: 84.3ms
467:	learn: 0.0721053	total: 1.2s	remaining: 81.8ms
468:	learn: 0.0718591	total: 1.2s	remaining: 79.2ms
469:	learn: 0.0715197	total: 1.2s	remaining: 76.6ms
470:	learn: 0.0712759	total: 1.2s	remaining: 74ms
471:	learn: 0.0710073	total: 1.21s	remaining: 71.5ms
472:	learn: 0.0707252	total: 1.21s	remaining: 68.9ms
473:	learn: 0.0704765	total: 1.21s	remaining: 66.4ms
474:	learn: 0.0702853	total: 1.21s	remaining: 63.8ms
475:	learn: 0.0700457	total: 1.21s	remaining: 61.2ms
476:	learn: 0.0697731	total: 1.22s	remaining: 58.7ms
477:	learn: 0.0695888	total: 1.22s	remaining: 56.1ms
478:	learn: 0.0693776	total: 1.22s	remaining: 53.5ms
479:	learn: 0.0691032	total: 1.22s	remaining: 51ms
480:	learn: 0.0689273	total: 1.23s	remaining: 48.4ms
481:	learn: 0.0687790	total: 1.23s	remaining: 45.9ms
482:	learn: 0.0686358	total: 1.23s	remaining: 43.3ms
483:	learn: 0.0683968	total: 1.23s	remaining: 40.8ms
484:	learn: 0.0681488	total: 1.24s	remaining: 38.2ms
485:	learn: 0.0679859	total: 1.24s	remaining: 35.6ms
486:	learn: 0.0677558	total: 1.24s	remaining: 33.1ms
487:	learn: 0.0675841	total: 1.24s	remaining: 30.5ms
488:	learn: 0.0673773	total: 1.24s	remaining: 28ms
489:	learn: 0.0671908	total: 1.25s	remaining: 25.5ms
490:	learn: 0.0668838	total: 1.25s	remaining: 22.9ms
491:	learn: 0.0666290	total: 1.25s	remaining: 20.4ms
492:	learn: 0.0663932	total: 1.25s	remaining: 17.8ms
493:	learn: 0.0661687	total: 1.26s	remaining: 15.3ms
494:	learn: 0.0659306	total: 1.26s	remaining: 12.7ms
495:	learn: 0.0657006	total: 1.26s	remaining: 10.2ms
496:	learn: 0.0654634	total: 1.26s	remaining: 7.63ms
497:	learn: 0.0652822	total: 1.27s	remaining: 5.09ms
498:	learn: 0.0650584	total: 1.27s	remaining: 2.54ms
499:	learn: 0.0648316	total: 1.27s	remaining: 0us
0:	learn: 0.6879319	total: 3.18ms	remaining: 1.59s
1:	learn: 0.6811708	total: 5.96ms	remaining: 1.49s
2:	learn: 0.6741590	total: 8.67ms	remaining: 1.44s
3:	learn: 0.6672862	total: 11.4ms	remaining: 1.41s
4:	learn: 0.6620954	total: 14.1ms	remaining: 1.4s
5:	learn: 0.6572332	total: 16.8ms	remaining: 1.38s
6:	learn: 0.6524892	total: 19.8ms	remaining: 1.39s
7:	learn: 0.6478280	total: 22.4ms	remaining: 1.38s
8:	learn: 0.6427620	total: 25.2ms	remaining: 1.38s
9:	learn: 0.6383509	total: 28ms	remaining: 1.37s
10:	learn: 0.6325974	total: 30.6ms	remaining: 1.36s
11:	learn: 0.6281795	total: 33.3ms	remaining: 1.35s
12:	learn: 0.6241452	total: 35.9ms	remaining: 1.34s
13:	learn: 0.6195992	total: 38.4ms	remaining: 1.33s
14:	learn: 0.6147182	total: 40.7ms	remaining: 1.32s
15:	learn: 0.6115072	total: 43ms	remaining: 1.3s
16:	learn: 0.6066352	total: 45.3ms	remaining: 1.29s
17:	learn: 0.6012858	total: 47.6ms	remaining: 1.27s
18:	learn: 0.5968383	total: 49.8ms	remaining: 1.26s
19:	learn: 0.5917763	total: 52ms	remaining: 1.25s
20:	learn: 0.5878842	total: 54.3ms	remaining: 1.24s
21:	learn: 0.5838657	total: 56.6ms	remaining: 1.23s
22:	learn: 0.5802977	total: 59.1ms	remaining: 1.23s
23:	learn: 0.5768331	total: 61.3ms	remaining: 1.22s
24:	learn: 0.5721892	total: 63.6ms	remaining: 1.21s
25:	learn: 0.5687532	total: 66ms	remaining: 1.2s
26:	learn: 0.5647076	total: 68.2ms	remaining: 1.19s
27:	learn: 0.5610750	total: 70.5ms	remaining: 1.19s
28:	learn: 0.5560719	total: 72.8ms	remaining: 1.18s
29:	learn: 0.5523974	total: 75.1ms	remaining: 1.18s
30:	learn: 0.5485525	total: 77.5ms	remaining: 1.17s
31:	learn: 0.5450087	total: 79.9ms	remaining: 1.17s
32:	learn: 0.5420131	total: 82.2ms	remaining: 1.16s
33:	learn: 0.5386744	total: 84.5ms	remaining: 1.16s
34:	learn: 0.5342453	total: 86.8ms	remaining: 1.15s
35:	learn: 0.5306550	total: 89.1ms	remaining: 1.15s
36:	learn: 0.5255439	total: 91.3ms	remaining: 1.14s
37:	learn: 0.5220310	total: 93.7ms	remaining: 1.14s
38:	learn: 0.5198043	total: 95.9ms	remaining: 1.13s
39:	learn: 0.5146828	total: 98.2ms	remaining: 1.13s
40:	learn: 0.5112932	total: 101ms	remaining: 1.13s
41:	learn: 0.5063308	total: 103ms	remaining: 1.13s
42:	learn: 0.5037577	total: 106ms	remaining: 1.12s
43:	learn: 0.4997387	total: 108ms	remaining: 1.12s
44:	learn: 0.4965812	total: 110ms	remaining: 1.11s
45:	learn: 0.4939084	total: 112ms	remaining: 1.11s
46:	learn: 0.4900042	total: 115ms	remaining: 1.11s
47:	learn: 0.4861264	total: 118ms	remaining: 1.11s
48:	learn: 0.4826530	total: 120ms	remaining: 1.1s
49:	learn: 0.4794936	total: 122ms	remaining: 1.1s
50:	learn: 0.4764567	total: 125ms	remaining: 1.1s
51:	learn: 0.4736915	total: 127ms	remaining: 1.09s
52:	learn: 0.4704348	total: 129ms	remaining: 1.09s
53:	learn: 0.4672396	total: 131ms	remaining: 1.08s
54:	learn: 0.4639134	total: 134ms	remaining: 1.08s
55:	learn: 0.4611599	total: 136ms	remaining: 1.08s
56:	learn: 0.4580021	total: 138ms	remaining: 1.07s
57:	learn: 0.4550602	total: 141ms	remaining: 1.07s
58:	learn: 0.4523680	total: 143ms	remaining: 1.07s
59:	learn: 0.4490698	total: 145ms	remaining: 1.06s
60:	learn: 0.4464845	total: 148ms	remaining: 1.06s
61:	learn: 0.4441383	total: 150ms	remaining: 1.06s
62:	learn: 0.4404010	total: 152ms	remaining: 1.06s
63:	learn: 0.4364124	total: 155ms	remaining: 1.05s
64:	learn: 0.4328805	total: 157ms	remaining: 1.05s
65:	learn: 0.4296145	total: 159ms	remaining: 1.05s
66:	learn: 0.4273321	total: 162ms	remaining: 1.04s
67:	learn: 0.4247299	total: 164ms	remaining: 1.04s
68:	learn: 0.4220441	total: 166ms	remaining: 1.04s
69:	learn: 0.4195130	total: 169ms	remaining: 1.03s
70:	learn: 0.4158425	total: 171ms	remaining: 1.03s
71:	learn: 0.4134377	total: 173ms	remaining: 1.03s
72:	learn: 0.4112305	total: 176ms	remaining: 1.03s
73:	learn: 0.4094079	total: 178ms	remaining: 1.02s
74:	learn: 0.4065178	total: 180ms	remaining: 1.02s
75:	learn: 0.4029178	total: 183ms	remaining: 1.02s
76:	learn: 0.4008257	total: 185ms	remaining: 1.01s
77:	learn: 0.3983311	total: 188ms	remaining: 1.01s
78:	learn: 0.3953094	total: 190ms	remaining: 1.01s
79:	learn: 0.3933456	total: 193ms	remaining: 1.01s
80:	learn: 0.3905863	total: 195ms	remaining: 1.01s
81:	learn: 0.3887237	total: 197ms	remaining: 1.01s
82:	learn: 0.3865646	total: 200ms	remaining: 1s
83:	learn: 0.3841853	total: 203ms	remaining: 1s
84:	learn: 0.3818323	total: 205ms	remaining: 1s
85:	learn: 0.3795551	total: 207ms	remaining: 998ms
86:	learn: 0.3772229	total: 210ms	remaining: 995ms
87:	learn: 0.3744254	total: 212ms	remaining: 992ms
88:	learn: 0.3728042	total: 214ms	remaining: 989ms
89:	learn: 0.3708355	total: 216ms	remaining: 986ms
90:	learn: 0.3689299	total: 219ms	remaining: 983ms
91:	learn: 0.3659250	total: 221ms	remaining: 980ms
92:	learn: 0.3644369	total: 223ms	remaining: 977ms
93:	learn: 0.3625363	total: 225ms	remaining: 974ms
94:	learn: 0.3601427	total: 228ms	remaining: 971ms
95:	learn: 0.3578461	total: 230ms	remaining: 968ms
96:	learn: 0.3558413	total: 232ms	remaining: 965ms
97:	learn: 0.3533919	total: 235ms	remaining: 962ms
98:	learn: 0.3512119	total: 237ms	remaining: 959ms
99:	learn: 0.3486625	total: 239ms	remaining: 956ms
100:	learn: 0.3469913	total: 241ms	remaining: 953ms
101:	learn: 0.3451221	total: 244ms	remaining: 951ms
102:	learn: 0.3429785	total: 246ms	remaining: 948ms
103:	learn: 0.3408848	total: 248ms	remaining: 945ms
104:	learn: 0.3395339	total: 251ms	remaining: 942ms
105:	learn: 0.3371280	total: 253ms	remaining: 940ms
106:	learn: 0.3351732	total: 255ms	remaining: 937ms
107:	learn: 0.3334702	total: 257ms	remaining: 934ms
108:	learn: 0.3316330	total: 260ms	remaining: 931ms
109:	learn: 0.3302531	total: 262ms	remaining: 928ms
110:	learn: 0.3288211	total: 264ms	remaining: 925ms
111:	learn: 0.3265782	total: 266ms	remaining: 923ms
112:	learn: 0.3247425	total: 269ms	remaining: 920ms
113:	learn: 0.3228750	total: 271ms	remaining: 917ms
114:	learn: 0.3205173	total: 273ms	remaining: 915ms
115:	learn: 0.3190680	total: 275ms	remaining: 912ms
116:	learn: 0.3174260	total: 278ms	remaining: 909ms
117:	learn: 0.3161104	total: 280ms	remaining: 906ms
118:	learn: 0.3144271	total: 282ms	remaining: 904ms
119:	learn: 0.3125907	total: 284ms	remaining: 901ms
120:	learn: 0.3106929	total: 287ms	remaining: 898ms
121:	learn: 0.3091652	total: 289ms	remaining: 896ms
122:	learn: 0.3071915	total: 291ms	remaining: 893ms
123:	learn: 0.3051683	total: 294ms	remaining: 890ms
124:	learn: 0.3034800	total: 296ms	remaining: 887ms
125:	learn: 0.3016997	total: 298ms	remaining: 885ms
126:	learn: 0.3001105	total: 300ms	remaining: 882ms
127:	learn: 0.2987900	total: 303ms	remaining: 879ms
128:	learn: 0.2973633	total: 305ms	remaining: 877ms
129:	learn: 0.2958819	total: 307ms	remaining: 874ms
130:	learn: 0.2946081	total: 310ms	remaining: 872ms
131:	learn: 0.2930647	total: 312ms	remaining: 870ms
132:	learn: 0.2916548	total: 314ms	remaining: 867ms
133:	learn: 0.2898857	total: 317ms	remaining: 865ms
134:	learn: 0.2885770	total: 319ms	remaining: 862ms
135:	learn: 0.2872137	total: 321ms	remaining: 859ms
136:	learn: 0.2860623	total: 323ms	remaining: 857ms
137:	learn: 0.2846944	total: 326ms	remaining: 854ms
138:	learn: 0.2828666	total: 328ms	remaining: 852ms
139:	learn: 0.2818899	total: 330ms	remaining: 849ms
140:	learn: 0.2809785	total: 333ms	remaining: 847ms
141:	learn: 0.2796534	total: 335ms	remaining: 844ms
142:	learn: 0.2788694	total: 337ms	remaining: 842ms
143:	learn: 0.2774532	total: 340ms	remaining: 839ms
144:	learn: 0.2759676	total: 342ms	remaining: 838ms
145:	learn: 0.2749997	total: 345ms	remaining: 835ms
146:	learn: 0.2734685	total: 347ms	remaining: 833ms
147:	learn: 0.2719069	total: 349ms	remaining: 831ms
148:	learn: 0.2705326	total: 352ms	remaining: 828ms
149:	learn: 0.2693001	total: 354ms	remaining: 826ms
150:	learn: 0.2679101	total: 356ms	remaining: 823ms
151:	learn: 0.2662614	total: 359ms	remaining: 821ms
152:	learn: 0.2651357	total: 361ms	remaining: 818ms
153:	learn: 0.2639978	total: 363ms	remaining: 816ms
154:	learn: 0.2625480	total: 366ms	remaining: 814ms
155:	learn: 0.2615064	total: 368ms	remaining: 811ms
156:	learn: 0.2601221	total: 370ms	remaining: 809ms
157:	learn: 0.2589990	total: 373ms	remaining: 806ms
158:	learn: 0.2576067	total: 375ms	remaining: 804ms
159:	learn: 0.2563781	total: 377ms	remaining: 801ms
160:	learn: 0.2556009	total: 380ms	remaining: 800ms
161:	learn: 0.2545280	total: 382ms	remaining: 798ms
162:	learn: 0.2534732	total: 385ms	remaining: 795ms
163:	learn: 0.2519983	total: 387ms	remaining: 793ms
164:	learn: 0.2507325	total: 389ms	remaining: 790ms
165:	learn: 0.2495561	total: 392ms	remaining: 789ms
166:	learn: 0.2485271	total: 396ms	remaining: 790ms
167:	learn: 0.2476350	total: 398ms	remaining: 787ms
168:	learn: 0.2461914	total: 401ms	remaining: 785ms
169:	learn: 0.2451767	total: 403ms	remaining: 783ms
170:	learn: 0.2441285	total: 406ms	remaining: 781ms
171:	learn: 0.2431435	total: 409ms	remaining: 779ms
172:	learn: 0.2422142	total: 411ms	remaining: 777ms
173:	learn: 0.2411681	total: 414ms	remaining: 775ms
174:	learn: 0.2399708	total: 416ms	remaining: 773ms
175:	learn: 0.2389772	total: 418ms	remaining: 770ms
176:	learn: 0.2378224	total: 421ms	remaining: 768ms
177:	learn: 0.2368116	total: 423ms	remaining: 766ms
178:	learn: 0.2353743	total: 426ms	remaining: 764ms
179:	learn: 0.2340836	total: 428ms	remaining: 761ms
180:	learn: 0.2328326	total: 431ms	remaining: 759ms
181:	learn: 0.2318901	total: 433ms	remaining: 757ms
182:	learn: 0.2310804	total: 436ms	remaining: 755ms
183:	learn: 0.2302220	total: 438ms	remaining: 753ms
184:	learn: 0.2298737	total: 441ms	remaining: 751ms
185:	learn: 0.2289778	total: 443ms	remaining: 748ms
186:	learn: 0.2279291	total: 446ms	remaining: 746ms
187:	learn: 0.2266632	total: 449ms	remaining: 745ms
188:	learn: 0.2251866	total: 451ms	remaining: 742ms
189:	learn: 0.2241792	total: 454ms	remaining: 740ms
190:	learn: 0.2228381	total: 456ms	remaining: 738ms
191:	learn: 0.2219449	total: 459ms	remaining: 736ms
192:	learn: 0.2209208	total: 461ms	remaining: 733ms
193:	learn: 0.2202305	total: 463ms	remaining: 731ms
194:	learn: 0.2191287	total: 466ms	remaining: 729ms
195:	learn: 0.2184773	total: 468ms	remaining: 726ms
196:	learn: 0.2176006	total: 471ms	remaining: 724ms
197:	learn: 0.2168336	total: 473ms	remaining: 722ms
198:	learn: 0.2159514	total: 476ms	remaining: 720ms
199:	learn: 0.2149374	total: 478ms	remaining: 717ms
200:	learn: 0.2140016	total: 481ms	remaining: 715ms
201:	learn: 0.2132393	total: 483ms	remaining: 712ms
202:	learn: 0.2121859	total: 485ms	remaining: 710ms
203:	learn: 0.2109836	total: 488ms	remaining: 708ms
204:	learn: 0.2100415	total: 490ms	remaining: 705ms
205:	learn: 0.2089575	total: 493ms	remaining: 703ms
206:	learn: 0.2083054	total: 495ms	remaining: 701ms
207:	learn: 0.2071443	total: 497ms	remaining: 698ms
208:	learn: 0.2061409	total: 500ms	remaining: 696ms
209:	learn: 0.2054433	total: 503ms	remaining: 694ms
210:	learn: 0.2048080	total: 506ms	remaining: 693ms
211:	learn: 0.2041548	total: 508ms	remaining: 691ms
212:	learn: 0.2030250	total: 511ms	remaining: 688ms
213:	learn: 0.2021241	total: 513ms	remaining: 686ms
214:	learn: 0.2011008	total: 515ms	remaining: 683ms
215:	learn: 0.2004160	total: 517ms	remaining: 680ms
216:	learn: 0.1996098	total: 520ms	remaining: 678ms
217:	learn: 0.1988337	total: 522ms	remaining: 675ms
218:	learn: 0.1977605	total: 525ms	remaining: 673ms
219:	learn: 0.1970862	total: 527ms	remaining: 671ms
220:	learn: 0.1961952	total: 529ms	remaining: 668ms
221:	learn: 0.1951981	total: 531ms	remaining: 665ms
222:	learn: 0.1945561	total: 534ms	remaining: 663ms
223:	learn: 0.1935994	total: 536ms	remaining: 660ms
224:	learn: 0.1925503	total: 538ms	remaining: 658ms
225:	learn: 0.1914036	total: 541ms	remaining: 655ms
226:	learn: 0.1905788	total: 543ms	remaining: 653ms
227:	learn: 0.1899215	total: 545ms	remaining: 650ms
228:	learn: 0.1891514	total: 547ms	remaining: 648ms
229:	learn: 0.1882312	total: 550ms	remaining: 645ms
230:	learn: 0.1875568	total: 552ms	remaining: 643ms
231:	learn: 0.1865975	total: 555ms	remaining: 641ms
232:	learn: 0.1857538	total: 557ms	remaining: 638ms
233:	learn: 0.1852423	total: 559ms	remaining: 636ms
234:	learn: 0.1844468	total: 561ms	remaining: 633ms
235:	learn: 0.1837601	total: 564ms	remaining: 631ms
236:	learn: 0.1830010	total: 566ms	remaining: 628ms
237:	learn: 0.1822428	total: 568ms	remaining: 626ms
238:	learn: 0.1815144	total: 571ms	remaining: 623ms
239:	learn: 0.1808450	total: 573ms	remaining: 621ms
240:	learn: 0.1802628	total: 575ms	remaining: 618ms
241:	learn: 0.1796038	total: 578ms	remaining: 616ms
242:	learn: 0.1789559	total: 580ms	remaining: 613ms
243:	learn: 0.1780805	total: 582ms	remaining: 611ms
244:	learn: 0.1773215	total: 584ms	remaining: 608ms
245:	learn: 0.1761974	total: 588ms	remaining: 607ms
246:	learn: 0.1754711	total: 591ms	remaining: 605ms
247:	learn: 0.1745539	total: 593ms	remaining: 603ms
248:	learn: 0.1736224	total: 595ms	remaining: 600ms
249:	learn: 0.1728387	total: 598ms	remaining: 598ms
250:	learn: 0.1720798	total: 600ms	remaining: 595ms
251:	learn: 0.1713833	total: 603ms	remaining: 593ms
252:	learn: 0.1707960	total: 605ms	remaining: 591ms
253:	learn: 0.1700412	total: 608ms	remaining: 589ms
254:	learn: 0.1693839	total: 611ms	remaining: 587ms
255:	learn: 0.1686994	total: 617ms	remaining: 588ms
256:	learn: 0.1679200	total: 619ms	remaining: 586ms
257:	learn: 0.1669641	total: 622ms	remaining: 584ms
258:	learn: 0.1663188	total: 625ms	remaining: 581ms
259:	learn: 0.1656098	total: 627ms	remaining: 579ms
260:	learn: 0.1650642	total: 630ms	remaining: 577ms
261:	learn: 0.1646438	total: 633ms	remaining: 575ms
262:	learn: 0.1639191	total: 635ms	remaining: 573ms
263:	learn: 0.1632800	total: 638ms	remaining: 570ms
264:	learn: 0.1626727	total: 641ms	remaining: 568ms
265:	learn: 0.1621887	total: 644ms	remaining: 566ms
266:	learn: 0.1614451	total: 646ms	remaining: 564ms
267:	learn: 0.1609252	total: 649ms	remaining: 562ms
268:	learn: 0.1603805	total: 652ms	remaining: 560ms
269:	learn: 0.1596943	total: 654ms	remaining: 557ms
270:	learn: 0.1591032	total: 657ms	remaining: 555ms
271:	learn: 0.1586318	total: 660ms	remaining: 553ms
272:	learn: 0.1582086	total: 662ms	remaining: 551ms
273:	learn: 0.1576457	total: 665ms	remaining: 548ms
274:	learn: 0.1567978	total: 668ms	remaining: 546ms
275:	learn: 0.1559380	total: 670ms	remaining: 544ms
276:	learn: 0.1553471	total: 673ms	remaining: 542ms
277:	learn: 0.1547885	total: 675ms	remaining: 539ms
278:	learn: 0.1541695	total: 678ms	remaining: 537ms
279:	learn: 0.1537363	total: 681ms	remaining: 535ms
280:	learn: 0.1530601	total: 684ms	remaining: 533ms
281:	learn: 0.1524586	total: 686ms	remaining: 531ms
282:	learn: 0.1517787	total: 688ms	remaining: 528ms
283:	learn: 0.1512355	total: 691ms	remaining: 525ms
284:	learn: 0.1507188	total: 693ms	remaining: 523ms
285:	learn: 0.1499896	total: 696ms	remaining: 521ms
286:	learn: 0.1494348	total: 699ms	remaining: 518ms
287:	learn: 0.1487351	total: 702ms	remaining: 517ms
288:	learn: 0.1478998	total: 705ms	remaining: 514ms
289:	learn: 0.1474091	total: 707ms	remaining: 512ms
290:	learn: 0.1470627	total: 709ms	remaining: 509ms
291:	learn: 0.1464907	total: 711ms	remaining: 507ms
292:	learn: 0.1458074	total: 714ms	remaining: 504ms
293:	learn: 0.1451245	total: 716ms	remaining: 502ms
294:	learn: 0.1446323	total: 718ms	remaining: 499ms
295:	learn: 0.1440774	total: 721ms	remaining: 497ms
296:	learn: 0.1436751	total: 723ms	remaining: 494ms
297:	learn: 0.1433156	total: 725ms	remaining: 491ms
298:	learn: 0.1430100	total: 727ms	remaining: 489ms
299:	learn: 0.1422732	total: 730ms	remaining: 486ms
300:	learn: 0.1415303	total: 732ms	remaining: 484ms
301:	learn: 0.1409764	total: 734ms	remaining: 481ms
302:	learn: 0.1405432	total: 736ms	remaining: 479ms
303:	learn: 0.1400905	total: 739ms	remaining: 476ms
304:	learn: 0.1396935	total: 741ms	remaining: 474ms
305:	learn: 0.1391915	total: 743ms	remaining: 471ms
306:	learn: 0.1386613	total: 745ms	remaining: 469ms
307:	learn: 0.1380197	total: 748ms	remaining: 466ms
308:	learn: 0.1375095	total: 750ms	remaining: 463ms
309:	learn: 0.1371848	total: 752ms	remaining: 461ms
310:	learn: 0.1365605	total: 754ms	remaining: 458ms
311:	learn: 0.1359087	total: 757ms	remaining: 456ms
312:	learn: 0.1354030	total: 759ms	remaining: 453ms
313:	learn: 0.1350147	total: 761ms	remaining: 451ms
314:	learn: 0.1344761	total: 764ms	remaining: 448ms
315:	learn: 0.1338320	total: 766ms	remaining: 446ms
316:	learn: 0.1333419	total: 768ms	remaining: 443ms
317:	learn: 0.1329047	total: 770ms	remaining: 441ms
318:	learn: 0.1324727	total: 773ms	remaining: 438ms
319:	learn: 0.1321009	total: 775ms	remaining: 436ms
320:	learn: 0.1317092	total: 777ms	remaining: 433ms
321:	learn: 0.1312706	total: 779ms	remaining: 431ms
322:	learn: 0.1306757	total: 782ms	remaining: 428ms
323:	learn: 0.1301248	total: 784ms	remaining: 426ms
324:	learn: 0.1296567	total: 786ms	remaining: 423ms
325:	learn: 0.1292053	total: 789ms	remaining: 421ms
326:	learn: 0.1286722	total: 791ms	remaining: 418ms
327:	learn: 0.1281599	total: 793ms	remaining: 416ms
328:	learn: 0.1278760	total: 795ms	remaining: 413ms
329:	learn: 0.1274205	total: 797ms	remaining: 411ms
330:	learn: 0.1270043	total: 800ms	remaining: 408ms
331:	learn: 0.1264395	total: 802ms	remaining: 406ms
332:	learn: 0.1260727	total: 804ms	remaining: 403ms
333:	learn: 0.1257457	total: 806ms	remaining: 401ms
334:	learn: 0.1252210	total: 809ms	remaining: 398ms
335:	learn: 0.1246782	total: 811ms	remaining: 396ms
336:	learn: 0.1242955	total: 813ms	remaining: 393ms
337:	learn: 0.1238152	total: 815ms	remaining: 391ms
338:	learn: 0.1233800	total: 818ms	remaining: 388ms
339:	learn: 0.1229734	total: 820ms	remaining: 386ms
340:	learn: 0.1226668	total: 822ms	remaining: 383ms
341:	learn: 0.1222464	total: 824ms	remaining: 381ms
342:	learn: 0.1217551	total: 827ms	remaining: 378ms
343:	learn: 0.1213749	total: 829ms	remaining: 376ms
344:	learn: 0.1209626	total: 831ms	remaining: 373ms
345:	learn: 0.1205231	total: 833ms	remaining: 371ms
346:	learn: 0.1201612	total: 836ms	remaining: 368ms
347:	learn: 0.1197066	total: 838ms	remaining: 366ms
348:	learn: 0.1192888	total: 840ms	remaining: 364ms
349:	learn: 0.1188069	total: 843ms	remaining: 361ms
350:	learn: 0.1185696	total: 845ms	remaining: 359ms
351:	learn: 0.1182043	total: 847ms	remaining: 356ms
352:	learn: 0.1177392	total: 849ms	remaining: 354ms
353:	learn: 0.1173563	total: 851ms	remaining: 351ms
354:	learn: 0.1170496	total: 854ms	remaining: 349ms
355:	learn: 0.1167893	total: 856ms	remaining: 346ms
356:	learn: 0.1163159	total: 858ms	remaining: 344ms
357:	learn: 0.1158559	total: 860ms	remaining: 341ms
358:	learn: 0.1155481	total: 863ms	remaining: 339ms
359:	learn: 0.1151685	total: 865ms	remaining: 336ms
360:	learn: 0.1147758	total: 867ms	remaining: 334ms
361:	learn: 0.1143993	total: 869ms	remaining: 331ms
362:	learn: 0.1140450	total: 871ms	remaining: 329ms
363:	learn: 0.1136524	total: 874ms	remaining: 326ms
364:	learn: 0.1133554	total: 878ms	remaining: 325ms
365:	learn: 0.1130357	total: 880ms	remaining: 322ms
366:	learn: 0.1126998	total: 882ms	remaining: 320ms
367:	learn: 0.1122813	total: 884ms	remaining: 317ms
368:	learn: 0.1118660	total: 888ms	remaining: 315ms
369:	learn: 0.1112886	total: 890ms	remaining: 313ms
370:	learn: 0.1108287	total: 892ms	remaining: 310ms
371:	learn: 0.1103989	total: 895ms	remaining: 308ms
372:	learn: 0.1099175	total: 897ms	remaining: 305ms
373:	learn: 0.1094126	total: 899ms	remaining: 303ms
374:	learn: 0.1091591	total: 902ms	remaining: 301ms
375:	learn: 0.1084543	total: 904ms	remaining: 298ms
376:	learn: 0.1080828	total: 906ms	remaining: 296ms
377:	learn: 0.1075707	total: 908ms	remaining: 293ms
378:	learn: 0.1071497	total: 911ms	remaining: 291ms
379:	learn: 0.1067795	total: 913ms	remaining: 288ms
380:	learn: 0.1063497	total: 915ms	remaining: 286ms
381:	learn: 0.1059369	total: 917ms	remaining: 283ms
382:	learn: 0.1056602	total: 920ms	remaining: 281ms
383:	learn: 0.1053730	total: 922ms	remaining: 279ms
384:	learn: 0.1050586	total: 924ms	remaining: 276ms
385:	learn: 0.1046657	total: 926ms	remaining: 274ms
386:	learn: 0.1043413	total: 929ms	remaining: 271ms
387:	learn: 0.1040577	total: 931ms	remaining: 269ms
388:	learn: 0.1037097	total: 933ms	remaining: 266ms
389:	learn: 0.1033181	total: 936ms	remaining: 264ms
390:	learn: 0.1029488	total: 938ms	remaining: 261ms
391:	learn: 0.1025947	total: 940ms	remaining: 259ms
392:	learn: 0.1022219	total: 942ms	remaining: 257ms
393:	learn: 0.1018688	total: 945ms	remaining: 254ms
394:	learn: 0.1016487	total: 947ms	remaining: 252ms
395:	learn: 0.1013219	total: 949ms	remaining: 249ms
396:	learn: 0.1008962	total: 952ms	remaining: 247ms
397:	learn: 0.1006356	total: 954ms	remaining: 244ms
398:	learn: 0.1003121	total: 956ms	remaining: 242ms
399:	learn: 0.1000564	total: 958ms	remaining: 240ms
400:	learn: 0.0996746	total: 961ms	remaining: 237ms
401:	learn: 0.0993689	total: 963ms	remaining: 235ms
402:	learn: 0.0988976	total: 965ms	remaining: 232ms
403:	learn: 0.0985383	total: 967ms	remaining: 230ms
404:	learn: 0.0982121	total: 970ms	remaining: 227ms
405:	learn: 0.0979430	total: 972ms	remaining: 225ms
406:	learn: 0.0975131	total: 975ms	remaining: 223ms
407:	learn: 0.0971258	total: 978ms	remaining: 220ms
408:	learn: 0.0968911	total: 980ms	remaining: 218ms
409:	learn: 0.0965840	total: 982ms	remaining: 216ms
410:	learn: 0.0962912	total: 984ms	remaining: 213ms
411:	learn: 0.0959425	total: 987ms	remaining: 211ms
412:	learn: 0.0956888	total: 989ms	remaining: 208ms
413:	learn: 0.0952739	total: 991ms	remaining: 206ms
414:	learn: 0.0949946	total: 993ms	remaining: 203ms
415:	learn: 0.0947438	total: 996ms	remaining: 201ms
416:	learn: 0.0943551	total: 998ms	remaining: 199ms
417:	learn: 0.0940216	total: 1s	remaining: 196ms
418:	learn: 0.0937643	total: 1s	remaining: 194ms
419:	learn: 0.0935492	total: 1s	remaining: 191ms
420:	learn: 0.0933730	total: 1.01s	remaining: 189ms
421:	learn: 0.0929307	total: 1.01s	remaining: 187ms
422:	learn: 0.0927430	total: 1.01s	remaining: 184ms
423:	learn: 0.0923816	total: 1.01s	remaining: 182ms
424:	learn: 0.0921377	total: 1.02s	remaining: 179ms
425:	learn: 0.0918510	total: 1.02s	remaining: 177ms
426:	learn: 0.0915432	total: 1.02s	remaining: 175ms
427:	learn: 0.0910790	total: 1.02s	remaining: 172ms
428:	learn: 0.0907351	total: 1.02s	remaining: 170ms
429:	learn: 0.0903968	total: 1.03s	remaining: 167ms
430:	learn: 0.0901774	total: 1.03s	remaining: 165ms
431:	learn: 0.0899166	total: 1.03s	remaining: 162ms
432:	learn: 0.0896298	total: 1.03s	remaining: 160ms
433:	learn: 0.0893549	total: 1.04s	remaining: 158ms
434:	learn: 0.0889910	total: 1.04s	remaining: 155ms
435:	learn: 0.0886464	total: 1.04s	remaining: 153ms
436:	learn: 0.0883209	total: 1.04s	remaining: 150ms
437:	learn: 0.0880270	total: 1.04s	remaining: 148ms
438:	learn: 0.0877908	total: 1.05s	remaining: 146ms
439:	learn: 0.0874473	total: 1.05s	remaining: 143ms
440:	learn: 0.0872500	total: 1.05s	remaining: 141ms
441:	learn: 0.0870667	total: 1.05s	remaining: 138ms
442:	learn: 0.0867939	total: 1.06s	remaining: 136ms
443:	learn: 0.0863977	total: 1.06s	remaining: 134ms
444:	learn: 0.0861735	total: 1.06s	remaining: 131ms
445:	learn: 0.0858154	total: 1.06s	remaining: 129ms
446:	learn: 0.0855675	total: 1.07s	remaining: 126ms
447:	learn: 0.0853664	total: 1.07s	remaining: 124ms
448:	learn: 0.0849855	total: 1.07s	remaining: 122ms
449:	learn: 0.0847126	total: 1.07s	remaining: 119ms
450:	learn: 0.0844799	total: 1.07s	remaining: 117ms
451:	learn: 0.0842154	total: 1.08s	remaining: 114ms
452:	learn: 0.0838933	total: 1.08s	remaining: 112ms
453:	learn: 0.0836573	total: 1.08s	remaining: 110ms
454:	learn: 0.0833699	total: 1.08s	remaining: 107ms
455:	learn: 0.0831055	total: 1.09s	remaining: 105ms
456:	learn: 0.0828867	total: 1.09s	remaining: 103ms
457:	learn: 0.0826563	total: 1.09s	remaining: 100ms
458:	learn: 0.0824277	total: 1.09s	remaining: 97.9ms
459:	learn: 0.0821188	total: 1.1s	remaining: 95.5ms
460:	learn: 0.0817316	total: 1.1s	remaining: 93.1ms
461:	learn: 0.0814659	total: 1.1s	remaining: 90.7ms
462:	learn: 0.0812799	total: 1.1s	remaining: 88.3ms
463:	learn: 0.0808998	total: 1.11s	remaining: 85.9ms
464:	learn: 0.0806313	total: 1.11s	remaining: 83.5ms
465:	learn: 0.0804226	total: 1.11s	remaining: 81.1ms
466:	learn: 0.0801646	total: 1.11s	remaining: 78.7ms
467:	learn: 0.0798655	total: 1.12s	remaining: 76.3ms
468:	learn: 0.0796565	total: 1.12s	remaining: 73.9ms
469:	learn: 0.0793636	total: 1.12s	remaining: 71.5ms
470:	learn: 0.0790929	total: 1.12s	remaining: 69.2ms
471:	learn: 0.0788840	total: 1.13s	remaining: 66.8ms
472:	learn: 0.0786314	total: 1.13s	remaining: 64.4ms
473:	learn: 0.0784301	total: 1.13s	remaining: 62ms
474:	learn: 0.0781471	total: 1.13s	remaining: 59.6ms
475:	learn: 0.0779521	total: 1.13s	remaining: 57.2ms
476:	learn: 0.0777346	total: 1.14s	remaining: 54.8ms
477:	learn: 0.0775292	total: 1.14s	remaining: 52.4ms
478:	learn: 0.0772202	total: 1.14s	remaining: 50ms
479:	learn: 0.0770365	total: 1.14s	remaining: 47.6ms
480:	learn: 0.0767842	total: 1.15s	remaining: 45.3ms
481:	learn: 0.0764819	total: 1.15s	remaining: 42.9ms
482:	learn: 0.0762835	total: 1.15s	remaining: 40.5ms
483:	learn: 0.0760126	total: 1.15s	remaining: 38.1ms
484:	learn: 0.0758444	total: 1.15s	remaining: 35.7ms
485:	learn: 0.0756206	total: 1.16s	remaining: 33.3ms
486:	learn: 0.0752819	total: 1.16s	remaining: 30.9ms
487:	learn: 0.0750537	total: 1.16s	remaining: 28.6ms
488:	learn: 0.0747462	total: 1.16s	remaining: 26.2ms
489:	learn: 0.0744821	total: 1.17s	remaining: 23.8ms
490:	learn: 0.0742343	total: 1.17s	remaining: 21.4ms
491:	learn: 0.0740300	total: 1.17s	remaining: 19ms
492:	learn: 0.0737849	total: 1.17s	remaining: 16.7ms
493:	learn: 0.0735607	total: 1.18s	remaining: 14.3ms
494:	learn: 0.0732882	total: 1.18s	remaining: 11.9ms
495:	learn: 0.0730238	total: 1.18s	remaining: 9.53ms
496:	learn: 0.0728231	total: 1.18s	remaining: 7.15ms
497:	learn: 0.0726002	total: 1.19s	remaining: 4.76ms
498:	learn: 0.0723415	total: 1.19s	remaining: 2.38ms
499:	learn: 0.0721168	total: 1.19s	remaining: 0us
0:	learn: 0.6876632	total: 2.94ms	remaining: 1.47s
1:	learn: 0.6816629	total: 5.64ms	remaining: 1.41s
2:	learn: 0.6753449	total: 8.31ms	remaining: 1.38s
3:	learn: 0.6688931	total: 10.8ms	remaining: 1.34s
4:	learn: 0.6633257	total: 13.3ms	remaining: 1.32s
5:	learn: 0.6580520	total: 15.8ms	remaining: 1.3s
6:	learn: 0.6527459	total: 18.4ms	remaining: 1.3s
7:	learn: 0.6473909	total: 21ms	remaining: 1.29s
8:	learn: 0.6422511	total: 23.5ms	remaining: 1.28s
9:	learn: 0.6380447	total: 26.1ms	remaining: 1.28s
10:	learn: 0.6327739	total: 28.7ms	remaining: 1.27s
11:	learn: 0.6277602	total: 31.3ms	remaining: 1.27s
12:	learn: 0.6244948	total: 33.7ms	remaining: 1.26s
13:	learn: 0.6195920	total: 36.3ms	remaining: 1.26s
14:	learn: 0.6150811	total: 38.9ms	remaining: 1.26s
15:	learn: 0.6103367	total: 41.6ms	remaining: 1.26s
16:	learn: 0.6052937	total: 44.1ms	remaining: 1.25s
17:	learn: 0.6006585	total: 46.7ms	remaining: 1.25s
18:	learn: 0.5956416	total: 49.3ms	remaining: 1.25s
19:	learn: 0.5916675	total: 51.8ms	remaining: 1.24s
20:	learn: 0.5864068	total: 54.4ms	remaining: 1.24s
21:	learn: 0.5811431	total: 57ms	remaining: 1.24s
22:	learn: 0.5764755	total: 59.6ms	remaining: 1.24s
23:	learn: 0.5726939	total: 62.1ms	remaining: 1.23s
24:	learn: 0.5687208	total: 64.4ms	remaining: 1.22s
25:	learn: 0.5645030	total: 66.6ms	remaining: 1.21s
26:	learn: 0.5601575	total: 68.8ms	remaining: 1.21s
27:	learn: 0.5563730	total: 71.1ms	remaining: 1.2s
28:	learn: 0.5514439	total: 73.4ms	remaining: 1.19s
29:	learn: 0.5473845	total: 75.7ms	remaining: 1.19s
30:	learn: 0.5437712	total: 78ms	remaining: 1.18s
31:	learn: 0.5406558	total: 80.3ms	remaining: 1.17s
32:	learn: 0.5358443	total: 82.5ms	remaining: 1.17s
33:	learn: 0.5315438	total: 84.8ms	remaining: 1.16s
34:	learn: 0.5270431	total: 87.1ms	remaining: 1.16s
35:	learn: 0.5226916	total: 89.3ms	remaining: 1.15s
36:	learn: 0.5191274	total: 91.5ms	remaining: 1.15s
37:	learn: 0.5161331	total: 93.8ms	remaining: 1.14s
38:	learn: 0.5123026	total: 96.8ms	remaining: 1.14s
39:	learn: 0.5092397	total: 99.6ms	remaining: 1.15s
40:	learn: 0.5053789	total: 102ms	remaining: 1.14s
41:	learn: 0.5017071	total: 104ms	remaining: 1.14s
42:	learn: 0.4987066	total: 107ms	remaining: 1.13s
43:	learn: 0.4950656	total: 109ms	remaining: 1.13s
44:	learn: 0.4921269	total: 113ms	remaining: 1.14s
45:	learn: 0.4896374	total: 115ms	remaining: 1.14s
46:	learn: 0.4858794	total: 117ms	remaining: 1.13s
47:	learn: 0.4822708	total: 120ms	remaining: 1.13s
48:	learn: 0.4780300	total: 122ms	remaining: 1.12s
49:	learn: 0.4746747	total: 124ms	remaining: 1.12s
50:	learn: 0.4716122	total: 127ms	remaining: 1.11s
51:	learn: 0.4686913	total: 129ms	remaining: 1.11s
52:	learn: 0.4657306	total: 131ms	remaining: 1.1s
53:	learn: 0.4623217	total: 133ms	remaining: 1.1s
54:	learn: 0.4594912	total: 136ms	remaining: 1.1s
55:	learn: 0.4563078	total: 138ms	remaining: 1.09s
56:	learn: 0.4537536	total: 140ms	remaining: 1.09s
57:	learn: 0.4503920	total: 142ms	remaining: 1.08s
58:	learn: 0.4467031	total: 145ms	remaining: 1.08s
59:	learn: 0.4435601	total: 147ms	remaining: 1.08s
60:	learn: 0.4390848	total: 149ms	remaining: 1.07s
61:	learn: 0.4347847	total: 152ms	remaining: 1.07s
62:	learn: 0.4312946	total: 154ms	remaining: 1.07s
63:	learn: 0.4275288	total: 156ms	remaining: 1.06s
64:	learn: 0.4248580	total: 158ms	remaining: 1.06s
65:	learn: 0.4215278	total: 161ms	remaining: 1.06s
66:	learn: 0.4186841	total: 163ms	remaining: 1.05s
67:	learn: 0.4153387	total: 165ms	remaining: 1.05s
68:	learn: 0.4125030	total: 168ms	remaining: 1.05s
69:	learn: 0.4089369	total: 170ms	remaining: 1.04s
70:	learn: 0.4063727	total: 172ms	remaining: 1.04s
71:	learn: 0.4045069	total: 174ms	remaining: 1.04s
72:	learn: 0.4021376	total: 177ms	remaining: 1.03s
73:	learn: 0.3996942	total: 179ms	remaining: 1.03s
74:	learn: 0.3968370	total: 181ms	remaining: 1.03s
75:	learn: 0.3934868	total: 184ms	remaining: 1.02s
76:	learn: 0.3914525	total: 186ms	remaining: 1.02s
77:	learn: 0.3887224	total: 188ms	remaining: 1.02s
78:	learn: 0.3868599	total: 190ms	remaining: 1.01s
79:	learn: 0.3843224	total: 193ms	remaining: 1.01s
80:	learn: 0.3813181	total: 195ms	remaining: 1.01s
81:	learn: 0.3786359	total: 197ms	remaining: 1s
82:	learn: 0.3762135	total: 199ms	remaining: 1s
83:	learn: 0.3734079	total: 202ms	remaining: 999ms
84:	learn: 0.3705946	total: 204ms	remaining: 995ms
85:	learn: 0.3679135	total: 206ms	remaining: 992ms
86:	learn: 0.3657714	total: 209ms	remaining: 994ms
87:	learn: 0.3633144	total: 212ms	remaining: 991ms
88:	learn: 0.3613192	total: 214ms	remaining: 988ms
89:	learn: 0.3599024	total: 216ms	remaining: 985ms
90:	learn: 0.3580617	total: 218ms	remaining: 982ms
91:	learn: 0.3551626	total: 221ms	remaining: 978ms
92:	learn: 0.3533424	total: 223ms	remaining: 975ms
93:	learn: 0.3516880	total: 225ms	remaining: 972ms
94:	learn: 0.3497965	total: 227ms	remaining: 969ms
95:	learn: 0.3482846	total: 230ms	remaining: 966ms
96:	learn: 0.3460545	total: 232ms	remaining: 964ms
97:	learn: 0.3440910	total: 235ms	remaining: 962ms
98:	learn: 0.3423015	total: 237ms	remaining: 960ms
99:	learn: 0.3397732	total: 239ms	remaining: 957ms
100:	learn: 0.3379167	total: 242ms	remaining: 954ms
101:	learn: 0.3360117	total: 244ms	remaining: 951ms
102:	learn: 0.3345140	total: 246ms	remaining: 949ms
103:	learn: 0.3322606	total: 249ms	remaining: 947ms
104:	learn: 0.3305000	total: 251ms	remaining: 943ms
105:	learn: 0.3289476	total: 253ms	remaining: 940ms
106:	learn: 0.3273678	total: 255ms	remaining: 938ms
107:	learn: 0.3255579	total: 258ms	remaining: 935ms
108:	learn: 0.3236222	total: 260ms	remaining: 932ms
109:	learn: 0.3217938	total: 262ms	remaining: 929ms
110:	learn: 0.3198327	total: 264ms	remaining: 926ms
111:	learn: 0.3177544	total: 267ms	remaining: 923ms
112:	learn: 0.3165069	total: 269ms	remaining: 921ms
113:	learn: 0.3143625	total: 271ms	remaining: 918ms
114:	learn: 0.3120176	total: 273ms	remaining: 915ms
115:	learn: 0.3110885	total: 276ms	remaining: 913ms
116:	learn: 0.3096779	total: 278ms	remaining: 910ms
117:	learn: 0.3084681	total: 280ms	remaining: 907ms
118:	learn: 0.3071986	total: 282ms	remaining: 904ms
119:	learn: 0.3051670	total: 285ms	remaining: 902ms
120:	learn: 0.3035292	total: 287ms	remaining: 900ms
121:	learn: 0.3020954	total: 289ms	remaining: 897ms
122:	learn: 0.3000438	total: 292ms	remaining: 894ms
123:	learn: 0.2986748	total: 294ms	remaining: 891ms
124:	learn: 0.2970908	total: 296ms	remaining: 889ms
125:	learn: 0.2957809	total: 299ms	remaining: 886ms
126:	learn: 0.2942730	total: 301ms	remaining: 884ms
127:	learn: 0.2929147	total: 305ms	remaining: 887ms
128:	learn: 0.2910970	total: 308ms	remaining: 885ms
129:	learn: 0.2892322	total: 310ms	remaining: 882ms
130:	learn: 0.2877535	total: 312ms	remaining: 879ms
131:	learn: 0.2862555	total: 314ms	remaining: 877ms
132:	learn: 0.2847323	total: 317ms	remaining: 874ms
133:	learn: 0.2834565	total: 319ms	remaining: 871ms
134:	learn: 0.2821358	total: 321ms	remaining: 868ms
135:	learn: 0.2811518	total: 323ms	remaining: 865ms
136:	learn: 0.2795091	total: 326ms	remaining: 863ms
137:	learn: 0.2784098	total: 328ms	remaining: 861ms
138:	learn: 0.2769408	total: 330ms	remaining: 858ms
139:	learn: 0.2753823	total: 333ms	remaining: 855ms
140:	learn: 0.2737031	total: 335ms	remaining: 852ms
141:	learn: 0.2724100	total: 337ms	remaining: 850ms
142:	learn: 0.2715913	total: 339ms	remaining: 847ms
143:	learn: 0.2703418	total: 341ms	remaining: 844ms
144:	learn: 0.2688584	total: 344ms	remaining: 842ms
145:	learn: 0.2674640	total: 346ms	remaining: 839ms
146:	learn: 0.2660288	total: 348ms	remaining: 836ms
147:	learn: 0.2647230	total: 350ms	remaining: 833ms
148:	learn: 0.2636560	total: 353ms	remaining: 831ms
149:	learn: 0.2626853	total: 355ms	remaining: 828ms
150:	learn: 0.2611047	total: 357ms	remaining: 826ms
151:	learn: 0.2598890	total: 360ms	remaining: 823ms
152:	learn: 0.2588534	total: 362ms	remaining: 820ms
153:	learn: 0.2576328	total: 364ms	remaining: 818ms
154:	learn: 0.2562510	total: 366ms	remaining: 815ms
155:	learn: 0.2552468	total: 369ms	remaining: 813ms
156:	learn: 0.2544522	total: 371ms	remaining: 810ms
157:	learn: 0.2530843	total: 373ms	remaining: 807ms
158:	learn: 0.2517395	total: 375ms	remaining: 805ms
159:	learn: 0.2506272	total: 377ms	remaining: 802ms
160:	learn: 0.2497215	total: 380ms	remaining: 800ms
161:	learn: 0.2485161	total: 382ms	remaining: 797ms
162:	learn: 0.2473681	total: 384ms	remaining: 794ms
163:	learn: 0.2460419	total: 386ms	remaining: 792ms
164:	learn: 0.2451665	total: 389ms	remaining: 789ms
165:	learn: 0.2437966	total: 391ms	remaining: 787ms
166:	learn: 0.2422821	total: 393ms	remaining: 784ms
167:	learn: 0.2412745	total: 396ms	remaining: 782ms
168:	learn: 0.2404163	total: 398ms	remaining: 779ms
169:	learn: 0.2390705	total: 400ms	remaining: 777ms
170:	learn: 0.2376067	total: 403ms	remaining: 775ms
171:	learn: 0.2364713	total: 405ms	remaining: 772ms
172:	learn: 0.2353807	total: 407ms	remaining: 770ms
173:	learn: 0.2343880	total: 410ms	remaining: 767ms
174:	learn: 0.2330111	total: 412ms	remaining: 765ms
175:	learn: 0.2324985	total: 414ms	remaining: 762ms
176:	learn: 0.2316732	total: 416ms	remaining: 760ms
177:	learn: 0.2301157	total: 419ms	remaining: 757ms
178:	learn: 0.2288364	total: 421ms	remaining: 755ms
179:	learn: 0.2280220	total: 423ms	remaining: 753ms
180:	learn: 0.2269716	total: 426ms	remaining: 750ms
181:	learn: 0.2257770	total: 428ms	remaining: 748ms
182:	learn: 0.2245864	total: 431ms	remaining: 746ms
183:	learn: 0.2238491	total: 433ms	remaining: 744ms
184:	learn: 0.2231571	total: 435ms	remaining: 741ms
185:	learn: 0.2223482	total: 437ms	remaining: 738ms
186:	learn: 0.2213476	total: 440ms	remaining: 736ms
187:	learn: 0.2204432	total: 442ms	remaining: 734ms
188:	learn: 0.2190754	total: 444ms	remaining: 731ms
189:	learn: 0.2183978	total: 446ms	remaining: 728ms
190:	learn: 0.2174913	total: 449ms	remaining: 726ms
191:	learn: 0.2166889	total: 451ms	remaining: 723ms
192:	learn: 0.2157236	total: 453ms	remaining: 721ms
193:	learn: 0.2149389	total: 455ms	remaining: 718ms
194:	learn: 0.2139768	total: 458ms	remaining: 716ms
195:	learn: 0.2129734	total: 460ms	remaining: 713ms
196:	learn: 0.2122105	total: 462ms	remaining: 711ms
197:	learn: 0.2113743	total: 464ms	remaining: 708ms
198:	learn: 0.2105967	total: 467ms	remaining: 706ms
199:	learn: 0.2095140	total: 469ms	remaining: 703ms
200:	learn: 0.2084480	total: 471ms	remaining: 701ms
201:	learn: 0.2078405	total: 473ms	remaining: 698ms
202:	learn: 0.2069033	total: 476ms	remaining: 696ms
203:	learn: 0.2059368	total: 478ms	remaining: 694ms
204:	learn: 0.2049935	total: 480ms	remaining: 691ms
205:	learn: 0.2041843	total: 483ms	remaining: 689ms
206:	learn: 0.2032204	total: 485ms	remaining: 687ms
207:	learn: 0.2021411	total: 488ms	remaining: 684ms
208:	learn: 0.2012610	total: 490ms	remaining: 682ms
209:	learn: 0.2004849	total: 492ms	remaining: 680ms
210:	learn: 0.1999513	total: 494ms	remaining: 677ms
211:	learn: 0.1992462	total: 497ms	remaining: 675ms
212:	learn: 0.1982911	total: 499ms	remaining: 673ms
213:	learn: 0.1972320	total: 502ms	remaining: 671ms
214:	learn: 0.1963495	total: 504ms	remaining: 668ms
215:	learn: 0.1953789	total: 506ms	remaining: 666ms
216:	learn: 0.1947791	total: 509ms	remaining: 663ms
217:	learn: 0.1938428	total: 511ms	remaining: 661ms
218:	learn: 0.1928928	total: 513ms	remaining: 658ms
219:	learn: 0.1917075	total: 515ms	remaining: 656ms
220:	learn: 0.1911099	total: 518ms	remaining: 653ms
221:	learn: 0.1900536	total: 520ms	remaining: 651ms
222:	learn: 0.1893364	total: 522ms	remaining: 649ms
223:	learn: 0.1887382	total: 525ms	remaining: 646ms
224:	learn: 0.1879086	total: 527ms	remaining: 644ms
225:	learn: 0.1870805	total: 529ms	remaining: 641ms
226:	learn: 0.1862560	total: 531ms	remaining: 639ms
227:	learn: 0.1854063	total: 534ms	remaining: 636ms
228:	learn: 0.1847103	total: 536ms	remaining: 634ms
229:	learn: 0.1838582	total: 538ms	remaining: 632ms
230:	learn: 0.1831715	total: 540ms	remaining: 629ms
231:	learn: 0.1823375	total: 543ms	remaining: 627ms
232:	learn: 0.1814222	total: 545ms	remaining: 624ms
233:	learn: 0.1806056	total: 547ms	remaining: 622ms
234:	learn: 0.1799338	total: 549ms	remaining: 620ms
235:	learn: 0.1792872	total: 552ms	remaining: 617ms
236:	learn: 0.1786550	total: 554ms	remaining: 615ms
237:	learn: 0.1779678	total: 556ms	remaining: 612ms
238:	learn: 0.1770323	total: 558ms	remaining: 610ms
239:	learn: 0.1763020	total: 561ms	remaining: 607ms
240:	learn: 0.1754343	total: 563ms	remaining: 605ms
241:	learn: 0.1747106	total: 565ms	remaining: 603ms
242:	learn: 0.1741490	total: 567ms	remaining: 600ms
243:	learn: 0.1735591	total: 570ms	remaining: 598ms
244:	learn: 0.1727803	total: 573ms	remaining: 596ms
245:	learn: 0.1720327	total: 575ms	remaining: 594ms
246:	learn: 0.1713956	total: 578ms	remaining: 592ms
247:	learn: 0.1707420	total: 580ms	remaining: 589ms
248:	learn: 0.1703351	total: 582ms	remaining: 587ms
249:	learn: 0.1695926	total: 584ms	remaining: 584ms
250:	learn: 0.1688961	total: 587ms	remaining: 582ms
251:	learn: 0.1683616	total: 589ms	remaining: 580ms
252:	learn: 0.1678298	total: 591ms	remaining: 577ms
253:	learn: 0.1673050	total: 594ms	remaining: 575ms
254:	learn: 0.1667003	total: 596ms	remaining: 573ms
255:	learn: 0.1658399	total: 599ms	remaining: 571ms
256:	learn: 0.1652114	total: 601ms	remaining: 568ms
257:	learn: 0.1645643	total: 603ms	remaining: 566ms
258:	learn: 0.1639809	total: 605ms	remaining: 563ms
259:	learn: 0.1633235	total: 608ms	remaining: 561ms
260:	learn: 0.1626476	total: 610ms	remaining: 559ms
261:	learn: 0.1621196	total: 612ms	remaining: 556ms
262:	learn: 0.1613868	total: 615ms	remaining: 554ms
263:	learn: 0.1606268	total: 617ms	remaining: 551ms
264:	learn: 0.1598650	total: 619ms	remaining: 549ms
265:	learn: 0.1593643	total: 621ms	remaining: 547ms
266:	learn: 0.1588606	total: 624ms	remaining: 544ms
267:	learn: 0.1582570	total: 626ms	remaining: 542ms
268:	learn: 0.1576333	total: 628ms	remaining: 540ms
269:	learn: 0.1568419	total: 631ms	remaining: 537ms
270:	learn: 0.1561162	total: 633ms	remaining: 535ms
271:	learn: 0.1553287	total: 635ms	remaining: 532ms
272:	learn: 0.1546687	total: 637ms	remaining: 530ms
273:	learn: 0.1539470	total: 640ms	remaining: 528ms
274:	learn: 0.1535114	total: 642ms	remaining: 525ms
275:	learn: 0.1528130	total: 644ms	remaining: 523ms
276:	learn: 0.1522303	total: 646ms	remaining: 520ms
277:	learn: 0.1516598	total: 649ms	remaining: 518ms
278:	learn: 0.1510237	total: 651ms	remaining: 515ms
279:	learn: 0.1505277	total: 653ms	remaining: 513ms
280:	learn: 0.1498704	total: 655ms	remaining: 511ms
281:	learn: 0.1492636	total: 658ms	remaining: 508ms
282:	learn: 0.1486312	total: 660ms	remaining: 506ms
283:	learn: 0.1481236	total: 662ms	remaining: 504ms
284:	learn: 0.1474765	total: 664ms	remaining: 501ms
285:	learn: 0.1468243	total: 668ms	remaining: 500ms
286:	learn: 0.1462983	total: 671ms	remaining: 498ms
287:	learn: 0.1457226	total: 674ms	remaining: 496ms
288:	learn: 0.1451557	total: 676ms	remaining: 494ms
289:	learn: 0.1446418	total: 678ms	remaining: 491ms
290:	learn: 0.1441735	total: 680ms	remaining: 489ms
291:	learn: 0.1437226	total: 683ms	remaining: 486ms
292:	learn: 0.1432617	total: 685ms	remaining: 484ms
293:	learn: 0.1428211	total: 687ms	remaining: 482ms
294:	learn: 0.1422920	total: 690ms	remaining: 479ms
295:	learn: 0.1416768	total: 692ms	remaining: 477ms
296:	learn: 0.1412126	total: 694ms	remaining: 474ms
297:	learn: 0.1407819	total: 696ms	remaining: 472ms
298:	learn: 0.1402951	total: 699ms	remaining: 470ms
299:	learn: 0.1397196	total: 701ms	remaining: 467ms
300:	learn: 0.1390585	total: 703ms	remaining: 465ms
301:	learn: 0.1386067	total: 705ms	remaining: 463ms
302:	learn: 0.1381114	total: 708ms	remaining: 460ms
303:	learn: 0.1374868	total: 710ms	remaining: 458ms
304:	learn: 0.1370213	total: 712ms	remaining: 455ms
305:	learn: 0.1364645	total: 714ms	remaining: 453ms
306:	learn: 0.1358965	total: 717ms	remaining: 451ms
307:	learn: 0.1352447	total: 719ms	remaining: 448ms
308:	learn: 0.1347983	total: 721ms	remaining: 446ms
309:	learn: 0.1343947	total: 724ms	remaining: 443ms
310:	learn: 0.1336713	total: 726ms	remaining: 441ms
311:	learn: 0.1331786	total: 728ms	remaining: 439ms
312:	learn: 0.1327130	total: 731ms	remaining: 437ms
313:	learn: 0.1323095	total: 733ms	remaining: 434ms
314:	learn: 0.1319225	total: 735ms	remaining: 432ms
315:	learn: 0.1313946	total: 738ms	remaining: 429ms
316:	learn: 0.1308659	total: 740ms	remaining: 427ms
317:	learn: 0.1304408	total: 742ms	remaining: 425ms
318:	learn: 0.1300187	total: 744ms	remaining: 422ms
319:	learn: 0.1293007	total: 747ms	remaining: 420ms
320:	learn: 0.1287764	total: 749ms	remaining: 418ms
321:	learn: 0.1284094	total: 751ms	remaining: 415ms
322:	learn: 0.1278892	total: 753ms	remaining: 413ms
323:	learn: 0.1274512	total: 756ms	remaining: 410ms
324:	learn: 0.1269834	total: 758ms	remaining: 408ms
325:	learn: 0.1265799	total: 760ms	remaining: 406ms
326:	learn: 0.1261440	total: 763ms	remaining: 403ms
327:	learn: 0.1257594	total: 765ms	remaining: 401ms
328:	learn: 0.1253816	total: 767ms	remaining: 399ms
329:	learn: 0.1249773	total: 769ms	remaining: 396ms
330:	learn: 0.1243791	total: 773ms	remaining: 395ms
331:	learn: 0.1239522	total: 776ms	remaining: 392ms
332:	learn: 0.1234501	total: 778ms	remaining: 390ms
333:	learn: 0.1231154	total: 781ms	remaining: 388ms
334:	learn: 0.1225805	total: 783ms	remaining: 386ms
335:	learn: 0.1222655	total: 786ms	remaining: 384ms
336:	learn: 0.1219033	total: 788ms	remaining: 381ms
337:	learn: 0.1213293	total: 791ms	remaining: 379ms
338:	learn: 0.1210786	total: 793ms	remaining: 377ms
339:	learn: 0.1207136	total: 796ms	remaining: 375ms
340:	learn: 0.1204589	total: 798ms	remaining: 372ms
341:	learn: 0.1200592	total: 801ms	remaining: 370ms
342:	learn: 0.1195714	total: 803ms	remaining: 368ms
343:	learn: 0.1190144	total: 806ms	remaining: 365ms
344:	learn: 0.1186552	total: 808ms	remaining: 363ms
345:	learn: 0.1182974	total: 811ms	remaining: 361ms
346:	learn: 0.1177360	total: 813ms	remaining: 359ms
347:	learn: 0.1172491	total: 816ms	remaining: 356ms
348:	learn: 0.1168309	total: 819ms	remaining: 354ms
349:	learn: 0.1163185	total: 821ms	remaining: 352ms
350:	learn: 0.1160130	total: 824ms	remaining: 350ms
351:	learn: 0.1157103	total: 826ms	remaining: 347ms
352:	learn: 0.1151861	total: 828ms	remaining: 345ms
353:	learn: 0.1147854	total: 831ms	remaining: 343ms
354:	learn: 0.1142728	total: 833ms	remaining: 340ms
355:	learn: 0.1139224	total: 836ms	remaining: 338ms
356:	learn: 0.1134056	total: 838ms	remaining: 336ms
357:	learn: 0.1130402	total: 841ms	remaining: 334ms
358:	learn: 0.1126774	total: 843ms	remaining: 331ms
359:	learn: 0.1124243	total: 846ms	remaining: 329ms
360:	learn: 0.1120360	total: 848ms	remaining: 327ms
361:	learn: 0.1117548	total: 851ms	remaining: 324ms
362:	learn: 0.1112937	total: 853ms	remaining: 322ms
363:	learn: 0.1109803	total: 856ms	remaining: 320ms
364:	learn: 0.1105448	total: 858ms	remaining: 317ms
365:	learn: 0.1102668	total: 860ms	remaining: 315ms
366:	learn: 0.1100082	total: 863ms	remaining: 313ms
367:	learn: 0.1095917	total: 865ms	remaining: 310ms
368:	learn: 0.1092961	total: 867ms	remaining: 308ms
369:	learn: 0.1088484	total: 870ms	remaining: 306ms
370:	learn: 0.1084898	total: 872ms	remaining: 303ms
371:	learn: 0.1081935	total: 874ms	remaining: 301ms
372:	learn: 0.1076985	total: 877ms	remaining: 298ms
373:	learn: 0.1073007	total: 879ms	remaining: 296ms
374:	learn: 0.1069825	total: 881ms	remaining: 294ms
375:	learn: 0.1065279	total: 884ms	remaining: 292ms
376:	learn: 0.1061397	total: 887ms	remaining: 289ms
377:	learn: 0.1056187	total: 889ms	remaining: 287ms
378:	learn: 0.1051163	total: 891ms	remaining: 285ms
379:	learn: 0.1044393	total: 894ms	remaining: 282ms
380:	learn: 0.1039056	total: 896ms	remaining: 280ms
381:	learn: 0.1035531	total: 899ms	remaining: 278ms
382:	learn: 0.1032266	total: 901ms	remaining: 275ms
383:	learn: 0.1028309	total: 903ms	remaining: 273ms
384:	learn: 0.1024325	total: 906ms	remaining: 271ms
385:	learn: 0.1020409	total: 908ms	remaining: 268ms
386:	learn: 0.1015891	total: 910ms	remaining: 266ms
387:	learn: 0.1012658	total: 913ms	remaining: 263ms
388:	learn: 0.1008288	total: 915ms	remaining: 261ms
389:	learn: 0.1004160	total: 917ms	remaining: 259ms
390:	learn: 0.1001589	total: 920ms	remaining: 256ms
391:	learn: 0.0998190	total: 922ms	remaining: 254ms
392:	learn: 0.0994920	total: 924ms	remaining: 252ms
393:	learn: 0.0991886	total: 927ms	remaining: 249ms
394:	learn: 0.0989532	total: 929ms	remaining: 247ms
395:	learn: 0.0986438	total: 932ms	remaining: 245ms
396:	learn: 0.0983405	total: 934ms	remaining: 242ms
397:	learn: 0.0979984	total: 936ms	remaining: 240ms
398:	learn: 0.0976915	total: 938ms	remaining: 238ms
399:	learn: 0.0972372	total: 941ms	remaining: 235ms
400:	learn: 0.0969661	total: 943ms	remaining: 233ms
401:	learn: 0.0966603	total: 945ms	remaining: 230ms
402:	learn: 0.0963279	total: 948ms	remaining: 228ms
403:	learn: 0.0961006	total: 950ms	remaining: 226ms
404:	learn: 0.0957476	total: 952ms	remaining: 223ms
405:	learn: 0.0954033	total: 954ms	remaining: 221ms
406:	learn: 0.0950033	total: 957ms	remaining: 219ms
407:	learn: 0.0946341	total: 959ms	remaining: 216ms
408:	learn: 0.0943938	total: 961ms	remaining: 214ms
409:	learn: 0.0941251	total: 964ms	remaining: 212ms
410:	learn: 0.0938869	total: 966ms	remaining: 209ms
411:	learn: 0.0934805	total: 968ms	remaining: 207ms
412:	learn: 0.0931838	total: 970ms	remaining: 204ms
413:	learn: 0.0928827	total: 973ms	remaining: 202ms
414:	learn: 0.0925754	total: 975ms	remaining: 200ms
415:	learn: 0.0923027	total: 980ms	remaining: 198ms
416:	learn: 0.0919727	total: 982ms	remaining: 195ms
417:	learn: 0.0916818	total: 984ms	remaining: 193ms
418:	learn: 0.0913361	total: 987ms	remaining: 191ms
419:	learn: 0.0910949	total: 989ms	remaining: 188ms
420:	learn: 0.0908258	total: 991ms	remaining: 186ms
421:	learn: 0.0905500	total: 994ms	remaining: 184ms
422:	learn: 0.0902142	total: 996ms	remaining: 181ms
423:	learn: 0.0899606	total: 998ms	remaining: 179ms
424:	learn: 0.0896962	total: 1s	remaining: 177ms
425:	learn: 0.0893403	total: 1s	remaining: 174ms
426:	learn: 0.0889923	total: 1s	remaining: 172ms
427:	learn: 0.0887292	total: 1.01s	remaining: 169ms
428:	learn: 0.0884677	total: 1.01s	remaining: 167ms
429:	learn: 0.0881952	total: 1.01s	remaining: 165ms
430:	learn: 0.0878967	total: 1.01s	remaining: 162ms
431:	learn: 0.0875890	total: 1.02s	remaining: 160ms
432:	learn: 0.0872969	total: 1.02s	remaining: 158ms
433:	learn: 0.0870385	total: 1.02s	remaining: 155ms
434:	learn: 0.0868160	total: 1.02s	remaining: 153ms
435:	learn: 0.0865432	total: 1.02s	remaining: 151ms
436:	learn: 0.0862197	total: 1.03s	remaining: 148ms
437:	learn: 0.0859361	total: 1.03s	remaining: 146ms
438:	learn: 0.0856676	total: 1.03s	remaining: 144ms
439:	learn: 0.0854404	total: 1.03s	remaining: 141ms
440:	learn: 0.0851579	total: 1.04s	remaining: 139ms
441:	learn: 0.0849110	total: 1.04s	remaining: 136ms
442:	learn: 0.0846353	total: 1.04s	remaining: 134ms
443:	learn: 0.0842605	total: 1.04s	remaining: 132ms
444:	learn: 0.0840523	total: 1.05s	remaining: 129ms
445:	learn: 0.0837987	total: 1.05s	remaining: 127ms
446:	learn: 0.0835474	total: 1.05s	remaining: 125ms
447:	learn: 0.0832547	total: 1.05s	remaining: 122ms
448:	learn: 0.0829276	total: 1.06s	remaining: 120ms
449:	learn: 0.0825748	total: 1.06s	remaining: 118ms
450:	learn: 0.0823310	total: 1.06s	remaining: 115ms
451:	learn: 0.0821236	total: 1.06s	remaining: 113ms
452:	learn: 0.0818105	total: 1.07s	remaining: 111ms
453:	learn: 0.0814031	total: 1.07s	remaining: 108ms
454:	learn: 0.0811714	total: 1.07s	remaining: 106ms
455:	learn: 0.0808896	total: 1.08s	remaining: 104ms
456:	learn: 0.0806270	total: 1.08s	remaining: 102ms
457:	learn: 0.0803166	total: 1.08s	remaining: 99.2ms
458:	learn: 0.0800979	total: 1.08s	remaining: 96.8ms
459:	learn: 0.0798636	total: 1.09s	remaining: 94.5ms
460:	learn: 0.0796094	total: 1.09s	remaining: 92.1ms
461:	learn: 0.0793127	total: 1.09s	remaining: 89.8ms
462:	learn: 0.0790422	total: 1.09s	remaining: 87.4ms
463:	learn: 0.0788291	total: 1.1s	remaining: 85.1ms
464:	learn: 0.0786735	total: 1.1s	remaining: 82.7ms
465:	learn: 0.0783711	total: 1.1s	remaining: 80.4ms
466:	learn: 0.0780802	total: 1.1s	remaining: 78ms
467:	learn: 0.0778438	total: 1.11s	remaining: 75.6ms
468:	learn: 0.0776075	total: 1.11s	remaining: 73.3ms
469:	learn: 0.0772813	total: 1.11s	remaining: 70.9ms
470:	learn: 0.0770074	total: 1.11s	remaining: 68.6ms
471:	learn: 0.0767573	total: 1.12s	remaining: 66.2ms
472:	learn: 0.0765170	total: 1.12s	remaining: 63.8ms
473:	learn: 0.0762970	total: 1.12s	remaining: 61.5ms
474:	learn: 0.0760759	total: 1.12s	remaining: 59.1ms
475:	learn: 0.0758653	total: 1.13s	remaining: 56.8ms
476:	learn: 0.0756267	total: 1.13s	remaining: 54.4ms
477:	learn: 0.0754632	total: 1.13s	remaining: 52ms
478:	learn: 0.0751884	total: 1.13s	remaining: 49.7ms
479:	learn: 0.0748148	total: 1.14s	remaining: 47.3ms
480:	learn: 0.0745865	total: 1.14s	remaining: 45ms
481:	learn: 0.0744138	total: 1.14s	remaining: 42.6ms
482:	learn: 0.0741942	total: 1.14s	remaining: 40.2ms
483:	learn: 0.0739283	total: 1.14s	remaining: 37.8ms
484:	learn: 0.0737477	total: 1.15s	remaining: 35.5ms
485:	learn: 0.0734852	total: 1.15s	remaining: 33.1ms
486:	learn: 0.0732258	total: 1.15s	remaining: 30.7ms
487:	learn: 0.0730500	total: 1.15s	remaining: 28.4ms
488:	learn: 0.0728092	total: 1.16s	remaining: 26ms
489:	learn: 0.0725610	total: 1.16s	remaining: 23.6ms
490:	learn: 0.0722126	total: 1.16s	remaining: 21.3ms
491:	learn: 0.0719964	total: 1.16s	remaining: 18.9ms
492:	learn: 0.0718197	total: 1.17s	remaining: 16.5ms
493:	learn: 0.0716585	total: 1.17s	remaining: 14.2ms
494:	learn: 0.0714717	total: 1.17s	remaining: 11.8ms
495:	learn: 0.0712138	total: 1.17s	remaining: 9.45ms
496:	learn: 0.0710184	total: 1.17s	remaining: 7.09ms
497:	learn: 0.0708004	total: 1.18s	remaining: 4.73ms
498:	learn: 0.0705756	total: 1.18s	remaining: 2.36ms
499:	learn: 0.0702889	total: 1.18s	remaining: 0us
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.875, 0.7777777777777778, 0.8888888888888888, 0.8888888888888888, 1.0, 1.0, 1.0, 0.7692307692307693, 1.0, 0.9090909090909091]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.8888888888888888, 0.8181818181818182, 0.8181818181818182, 0.8181818181818182, 0.9090909090909091, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.7777777777777777, 0.823529411764706, 0.8888888888888888, 0.8888888888888888, 0.9, 0.9, 0.9, 0.8333333333333333, 0.8, 0.9090909090909091]
Model valid train Accuracy of flod: [0.8620689655172413, 0.896551724137931, 0.9310344827586207, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.8571428571428571, 0.8571428571428571, 0.9285714285714286]
模型Valid集Precision:0.9108877233877232,Recall:0.8292171717171717,F1_Score:0.8621509209744505,Accuracy:0.9046798029556651
0:	learn: 0.6887564	total: 4.1ms	remaining: 2.04s
1:	learn: 0.6836351	total: 6.49ms	remaining: 1.62s
2:	learn: 0.6784810	total: 8.95ms	remaining: 1.48s
3:	learn: 0.6722777	total: 11.4ms	remaining: 1.41s
4:	learn: 0.6674568	total: 13.7ms	remaining: 1.36s
5:	learn: 0.6618205	total: 16.1ms	remaining: 1.33s
6:	learn: 0.6573681	total: 18.6ms	remaining: 1.31s
7:	learn: 0.6515681	total: 21.1ms	remaining: 1.3s
8:	learn: 0.6460615	total: 23.6ms	remaining: 1.28s
9:	learn: 0.6417131	total: 25.9ms	remaining: 1.27s
10:	learn: 0.6353348	total: 28.3ms	remaining: 1.26s
11:	learn: 0.6303920	total: 30.6ms	remaining: 1.24s
12:	learn: 0.6274115	total: 32.9ms	remaining: 1.23s
13:	learn: 0.6224202	total: 35.3ms	remaining: 1.22s
14:	learn: 0.6181285	total: 37.9ms	remaining: 1.22s
15:	learn: 0.6131774	total: 40.3ms	remaining: 1.22s
16:	learn: 0.6086640	total: 42.7ms	remaining: 1.21s
17:	learn: 0.6037273	total: 45.2ms	remaining: 1.21s
18:	learn: 0.5982742	total: 48.6ms	remaining: 1.23s
19:	learn: 0.5943113	total: 51.3ms	remaining: 1.23s
20:	learn: 0.5895912	total: 53.7ms	remaining: 1.22s
21:	learn: 0.5843241	total: 56.1ms	remaining: 1.22s
22:	learn: 0.5805815	total: 58.7ms	remaining: 1.22s
23:	learn: 0.5767497	total: 61ms	remaining: 1.21s
24:	learn: 0.5725923	total: 63.4ms	remaining: 1.21s
25:	learn: 0.5682064	total: 65.8ms	remaining: 1.2s
26:	learn: 0.5642611	total: 68.2ms	remaining: 1.19s
27:	learn: 0.5607677	total: 70.7ms	remaining: 1.19s
28:	learn: 0.5563822	total: 73.1ms	remaining: 1.19s
29:	learn: 0.5525044	total: 75.4ms	remaining: 1.18s
30:	learn: 0.5482808	total: 78ms	remaining: 1.18s
31:	learn: 0.5456128	total: 80.5ms	remaining: 1.18s
32:	learn: 0.5417876	total: 82.8ms	remaining: 1.17s
33:	learn: 0.5371370	total: 85.1ms	remaining: 1.17s
34:	learn: 0.5341556	total: 87.5ms	remaining: 1.16s
35:	learn: 0.5297760	total: 90ms	remaining: 1.16s
36:	learn: 0.5256523	total: 92.4ms	remaining: 1.16s
37:	learn: 0.5222358	total: 94.8ms	remaining: 1.15s
38:	learn: 0.5183866	total: 97.4ms	remaining: 1.15s
39:	learn: 0.5143136	total: 99.7ms	remaining: 1.15s
40:	learn: 0.5111283	total: 102ms	remaining: 1.14s
41:	learn: 0.5067430	total: 104ms	remaining: 1.14s
42:	learn: 0.5037953	total: 107ms	remaining: 1.14s
43:	learn: 0.5002655	total: 109ms	remaining: 1.13s
44:	learn: 0.4958958	total: 112ms	remaining: 1.13s
45:	learn: 0.4928986	total: 114ms	remaining: 1.12s
46:	learn: 0.4891607	total: 117ms	remaining: 1.13s
47:	learn: 0.4851502	total: 119ms	remaining: 1.12s
48:	learn: 0.4819218	total: 122ms	remaining: 1.12s
49:	learn: 0.4792615	total: 124ms	remaining: 1.11s
50:	learn: 0.4754045	total: 126ms	remaining: 1.11s
51:	learn: 0.4726178	total: 129ms	remaining: 1.11s
52:	learn: 0.4698918	total: 131ms	remaining: 1.1s
53:	learn: 0.4658492	total: 133ms	remaining: 1.1s
54:	learn: 0.4628907	total: 136ms	remaining: 1.1s
55:	learn: 0.4609830	total: 138ms	remaining: 1.1s
56:	learn: 0.4579049	total: 141ms	remaining: 1.09s
57:	learn: 0.4548761	total: 143ms	remaining: 1.09s
58:	learn: 0.4514980	total: 146ms	remaining: 1.09s
59:	learn: 0.4484299	total: 148ms	remaining: 1.09s
60:	learn: 0.4453353	total: 151ms	remaining: 1.08s
61:	learn: 0.4426119	total: 153ms	remaining: 1.08s
62:	learn: 0.4396805	total: 156ms	remaining: 1.08s
63:	learn: 0.4359204	total: 158ms	remaining: 1.08s
64:	learn: 0.4335507	total: 160ms	remaining: 1.07s
65:	learn: 0.4304150	total: 163ms	remaining: 1.07s
66:	learn: 0.4273656	total: 165ms	remaining: 1.07s
67:	learn: 0.4245268	total: 168ms	remaining: 1.06s
68:	learn: 0.4223382	total: 170ms	remaining: 1.06s
69:	learn: 0.4190856	total: 172ms	remaining: 1.06s
70:	learn: 0.4168782	total: 175ms	remaining: 1.06s
71:	learn: 0.4142352	total: 177ms	remaining: 1.05s
72:	learn: 0.4114493	total: 180ms	remaining: 1.05s
73:	learn: 0.4092622	total: 182ms	remaining: 1.05s
74:	learn: 0.4065694	total: 184ms	remaining: 1.04s
75:	learn: 0.4025314	total: 187ms	remaining: 1.04s
76:	learn: 0.3999192	total: 189ms	remaining: 1.04s
77:	learn: 0.3970185	total: 192ms	remaining: 1.04s
78:	learn: 0.3945029	total: 195ms	remaining: 1.04s
79:	learn: 0.3922690	total: 197ms	remaining: 1.03s
80:	learn: 0.3904744	total: 200ms	remaining: 1.03s
81:	learn: 0.3885579	total: 202ms	remaining: 1.03s
82:	learn: 0.3858182	total: 204ms	remaining: 1.03s
83:	learn: 0.3834694	total: 207ms	remaining: 1.02s
84:	learn: 0.3813226	total: 209ms	remaining: 1.02s
85:	learn: 0.3790069	total: 211ms	remaining: 1.02s
86:	learn: 0.3768277	total: 214ms	remaining: 1.01s
87:	learn: 0.3746075	total: 216ms	remaining: 1.01s
88:	learn: 0.3726921	total: 219ms	remaining: 1.01s
89:	learn: 0.3705859	total: 221ms	remaining: 1.01s
90:	learn: 0.3685056	total: 224ms	remaining: 1s
91:	learn: 0.3659822	total: 226ms	remaining: 1s
92:	learn: 0.3640469	total: 228ms	remaining: 999ms
93:	learn: 0.3625158	total: 230ms	remaining: 995ms
94:	learn: 0.3606707	total: 233ms	remaining: 994ms
95:	learn: 0.3586471	total: 236ms	remaining: 994ms
96:	learn: 0.3560120	total: 239ms	remaining: 992ms
97:	learn: 0.3540221	total: 241ms	remaining: 989ms
98:	learn: 0.3521352	total: 244ms	remaining: 986ms
99:	learn: 0.3495557	total: 246ms	remaining: 984ms
100:	learn: 0.3474941	total: 248ms	remaining: 981ms
101:	learn: 0.3460280	total: 251ms	remaining: 979ms
102:	learn: 0.3440344	total: 253ms	remaining: 976ms
103:	learn: 0.3420485	total: 256ms	remaining: 974ms
104:	learn: 0.3406154	total: 258ms	remaining: 971ms
105:	learn: 0.3381433	total: 261ms	remaining: 969ms
106:	learn: 0.3361788	total: 263ms	remaining: 966ms
107:	learn: 0.3342945	total: 265ms	remaining: 963ms
108:	learn: 0.3314088	total: 268ms	remaining: 961ms
109:	learn: 0.3296575	total: 270ms	remaining: 958ms
110:	learn: 0.3276248	total: 273ms	remaining: 956ms
111:	learn: 0.3259110	total: 275ms	remaining: 954ms
112:	learn: 0.3242083	total: 278ms	remaining: 951ms
113:	learn: 0.3222682	total: 280ms	remaining: 948ms
114:	learn: 0.3202644	total: 282ms	remaining: 946ms
115:	learn: 0.3188541	total: 285ms	remaining: 943ms
116:	learn: 0.3174261	total: 287ms	remaining: 940ms
117:	learn: 0.3158670	total: 290ms	remaining: 938ms
118:	learn: 0.3145421	total: 292ms	remaining: 935ms
119:	learn: 0.3131146	total: 295ms	remaining: 933ms
120:	learn: 0.3109225	total: 297ms	remaining: 930ms
121:	learn: 0.3092389	total: 299ms	remaining: 928ms
122:	learn: 0.3072076	total: 302ms	remaining: 925ms
123:	learn: 0.3055357	total: 304ms	remaining: 923ms
124:	learn: 0.3037724	total: 307ms	remaining: 920ms
125:	learn: 0.3020199	total: 309ms	remaining: 917ms
126:	learn: 0.3004347	total: 312ms	remaining: 915ms
127:	learn: 0.2988319	total: 314ms	remaining: 913ms
128:	learn: 0.2975967	total: 316ms	remaining: 910ms
129:	learn: 0.2961530	total: 319ms	remaining: 907ms
130:	learn: 0.2950281	total: 321ms	remaining: 905ms
131:	learn: 0.2935432	total: 324ms	remaining: 902ms
132:	learn: 0.2915700	total: 326ms	remaining: 900ms
133:	learn: 0.2897616	total: 328ms	remaining: 897ms
134:	learn: 0.2881404	total: 331ms	remaining: 894ms
135:	learn: 0.2869002	total: 333ms	remaining: 892ms
136:	learn: 0.2851651	total: 336ms	remaining: 889ms
137:	learn: 0.2836978	total: 338ms	remaining: 887ms
138:	learn: 0.2820073	total: 340ms	remaining: 884ms
139:	learn: 0.2803024	total: 343ms	remaining: 881ms
140:	learn: 0.2790457	total: 345ms	remaining: 879ms
141:	learn: 0.2776057	total: 348ms	remaining: 876ms
142:	learn: 0.2761686	total: 350ms	remaining: 874ms
143:	learn: 0.2746586	total: 352ms	remaining: 871ms
144:	learn: 0.2729893	total: 355ms	remaining: 869ms
145:	learn: 0.2716774	total: 357ms	remaining: 866ms
146:	learn: 0.2702805	total: 359ms	remaining: 863ms
147:	learn: 0.2686742	total: 362ms	remaining: 861ms
148:	learn: 0.2675757	total: 364ms	remaining: 858ms
149:	learn: 0.2662182	total: 367ms	remaining: 856ms
150:	learn: 0.2651194	total: 369ms	remaining: 853ms
151:	learn: 0.2634962	total: 372ms	remaining: 851ms
152:	learn: 0.2624770	total: 374ms	remaining: 848ms
153:	learn: 0.2614550	total: 376ms	remaining: 846ms
154:	learn: 0.2601082	total: 379ms	remaining: 843ms
155:	learn: 0.2591438	total: 381ms	remaining: 840ms
156:	learn: 0.2581671	total: 383ms	remaining: 838ms
157:	learn: 0.2567885	total: 386ms	remaining: 836ms
158:	learn: 0.2556081	total: 389ms	remaining: 834ms
159:	learn: 0.2546066	total: 392ms	remaining: 832ms
160:	learn: 0.2534203	total: 394ms	remaining: 829ms
161:	learn: 0.2519014	total: 396ms	remaining: 827ms
162:	learn: 0.2510081	total: 399ms	remaining: 824ms
163:	learn: 0.2499424	total: 401ms	remaining: 822ms
164:	learn: 0.2486694	total: 404ms	remaining: 819ms
165:	learn: 0.2476372	total: 406ms	remaining: 817ms
166:	learn: 0.2463620	total: 408ms	remaining: 814ms
167:	learn: 0.2451357	total: 411ms	remaining: 812ms
168:	learn: 0.2444540	total: 413ms	remaining: 809ms
169:	learn: 0.2434588	total: 416ms	remaining: 807ms
170:	learn: 0.2424421	total: 418ms	remaining: 804ms
171:	learn: 0.2413431	total: 420ms	remaining: 802ms
172:	learn: 0.2403649	total: 423ms	remaining: 799ms
173:	learn: 0.2395547	total: 425ms	remaining: 797ms
174:	learn: 0.2385057	total: 428ms	remaining: 794ms
175:	learn: 0.2371633	total: 431ms	remaining: 794ms
176:	learn: 0.2363535	total: 434ms	remaining: 791ms
177:	learn: 0.2350591	total: 436ms	remaining: 789ms
178:	learn: 0.2340777	total: 439ms	remaining: 786ms
179:	learn: 0.2332547	total: 441ms	remaining: 784ms
180:	learn: 0.2319338	total: 443ms	remaining: 782ms
181:	learn: 0.2310151	total: 446ms	remaining: 779ms
182:	learn: 0.2300524	total: 448ms	remaining: 776ms
183:	learn: 0.2292266	total: 450ms	remaining: 774ms
184:	learn: 0.2285501	total: 453ms	remaining: 771ms
185:	learn: 0.2278859	total: 455ms	remaining: 768ms
186:	learn: 0.2267924	total: 458ms	remaining: 766ms
187:	learn: 0.2257312	total: 460ms	remaining: 763ms
188:	learn: 0.2245562	total: 462ms	remaining: 761ms
189:	learn: 0.2239152	total: 465ms	remaining: 758ms
190:	learn: 0.2227545	total: 467ms	remaining: 756ms
191:	learn: 0.2219392	total: 469ms	remaining: 753ms
192:	learn: 0.2208816	total: 472ms	remaining: 750ms
193:	learn: 0.2201771	total: 474ms	remaining: 748ms
194:	learn: 0.2191517	total: 476ms	remaining: 745ms
195:	learn: 0.2179391	total: 479ms	remaining: 743ms
196:	learn: 0.2170362	total: 481ms	remaining: 740ms
197:	learn: 0.2162765	total: 484ms	remaining: 738ms
198:	learn: 0.2150127	total: 486ms	remaining: 735ms
199:	learn: 0.2140719	total: 488ms	remaining: 732ms
200:	learn: 0.2127834	total: 491ms	remaining: 730ms
201:	learn: 0.2119869	total: 493ms	remaining: 727ms
202:	learn: 0.2110094	total: 495ms	remaining: 725ms
203:	learn: 0.2098574	total: 498ms	remaining: 722ms
204:	learn: 0.2091665	total: 500ms	remaining: 720ms
205:	learn: 0.2083334	total: 502ms	remaining: 717ms
206:	learn: 0.2074633	total: 505ms	remaining: 715ms
207:	learn: 0.2063928	total: 508ms	remaining: 713ms
208:	learn: 0.2052297	total: 510ms	remaining: 711ms
209:	learn: 0.2043757	total: 513ms	remaining: 708ms
210:	learn: 0.2034627	total: 515ms	remaining: 706ms
211:	learn: 0.2024348	total: 518ms	remaining: 703ms
212:	learn: 0.2015260	total: 520ms	remaining: 701ms
213:	learn: 0.2004147	total: 522ms	remaining: 698ms
214:	learn: 0.1995223	total: 525ms	remaining: 695ms
215:	learn: 0.1985342	total: 527ms	remaining: 693ms
216:	learn: 0.1977239	total: 530ms	remaining: 691ms
217:	learn: 0.1966927	total: 532ms	remaining: 688ms
218:	learn: 0.1954789	total: 534ms	remaining: 686ms
219:	learn: 0.1946895	total: 537ms	remaining: 683ms
220:	learn: 0.1940930	total: 539ms	remaining: 681ms
221:	learn: 0.1931043	total: 542ms	remaining: 678ms
222:	learn: 0.1923664	total: 544ms	remaining: 676ms
223:	learn: 0.1916957	total: 546ms	remaining: 673ms
224:	learn: 0.1907635	total: 549ms	remaining: 671ms
225:	learn: 0.1900803	total: 551ms	remaining: 668ms
226:	learn: 0.1891288	total: 554ms	remaining: 666ms
227:	learn: 0.1883585	total: 556ms	remaining: 663ms
228:	learn: 0.1875274	total: 558ms	remaining: 661ms
229:	learn: 0.1867107	total: 561ms	remaining: 658ms
230:	learn: 0.1859502	total: 563ms	remaining: 656ms
231:	learn: 0.1847943	total: 566ms	remaining: 654ms
232:	learn: 0.1841112	total: 568ms	remaining: 651ms
233:	learn: 0.1831401	total: 571ms	remaining: 649ms
234:	learn: 0.1824607	total: 573ms	remaining: 646ms
235:	learn: 0.1816262	total: 575ms	remaining: 644ms
236:	learn: 0.1808029	total: 578ms	remaining: 641ms
237:	learn: 0.1803718	total: 580ms	remaining: 639ms
238:	learn: 0.1798689	total: 583ms	remaining: 637ms
239:	learn: 0.1791648	total: 586ms	remaining: 634ms
240:	learn: 0.1785865	total: 588ms	remaining: 632ms
241:	learn: 0.1779534	total: 590ms	remaining: 630ms
242:	learn: 0.1773739	total: 593ms	remaining: 627ms
243:	learn: 0.1765619	total: 595ms	remaining: 624ms
244:	learn: 0.1757008	total: 597ms	remaining: 622ms
245:	learn: 0.1749599	total: 600ms	remaining: 619ms
246:	learn: 0.1742830	total: 602ms	remaining: 617ms
247:	learn: 0.1734397	total: 605ms	remaining: 614ms
248:	learn: 0.1730240	total: 607ms	remaining: 612ms
249:	learn: 0.1722762	total: 609ms	remaining: 609ms
250:	learn: 0.1718014	total: 612ms	remaining: 607ms
251:	learn: 0.1712349	total: 614ms	remaining: 604ms
252:	learn: 0.1707420	total: 616ms	remaining: 602ms
253:	learn: 0.1700693	total: 619ms	remaining: 599ms
254:	learn: 0.1694360	total: 621ms	remaining: 597ms
255:	learn: 0.1689321	total: 624ms	remaining: 594ms
256:	learn: 0.1681537	total: 626ms	remaining: 592ms
257:	learn: 0.1675217	total: 629ms	remaining: 590ms
258:	learn: 0.1668586	total: 631ms	remaining: 587ms
259:	learn: 0.1664485	total: 633ms	remaining: 585ms
260:	learn: 0.1659444	total: 636ms	remaining: 582ms
261:	learn: 0.1653784	total: 638ms	remaining: 580ms
262:	learn: 0.1647210	total: 641ms	remaining: 577ms
263:	learn: 0.1641760	total: 643ms	remaining: 575ms
264:	learn: 0.1634366	total: 645ms	remaining: 572ms
265:	learn: 0.1629723	total: 648ms	remaining: 570ms
266:	learn: 0.1623456	total: 650ms	remaining: 567ms
267:	learn: 0.1618733	total: 653ms	remaining: 565ms
268:	learn: 0.1613066	total: 655ms	remaining: 562ms
269:	learn: 0.1607545	total: 657ms	remaining: 560ms
270:	learn: 0.1601202	total: 660ms	remaining: 557ms
271:	learn: 0.1595016	total: 662ms	remaining: 555ms
272:	learn: 0.1590426	total: 665ms	remaining: 553ms
273:	learn: 0.1585920	total: 667ms	remaining: 550ms
274:	learn: 0.1579504	total: 669ms	remaining: 548ms
275:	learn: 0.1571960	total: 672ms	remaining: 545ms
276:	learn: 0.1566434	total: 674ms	remaining: 543ms
277:	learn: 0.1562019	total: 677ms	remaining: 540ms
278:	learn: 0.1557025	total: 679ms	remaining: 538ms
279:	learn: 0.1551870	total: 681ms	remaining: 535ms
280:	learn: 0.1546353	total: 684ms	remaining: 533ms
281:	learn: 0.1541151	total: 686ms	remaining: 530ms
282:	learn: 0.1534675	total: 689ms	remaining: 528ms
283:	learn: 0.1529403	total: 691ms	remaining: 526ms
284:	learn: 0.1524716	total: 693ms	remaining: 523ms
285:	learn: 0.1520423	total: 696ms	remaining: 521ms
286:	learn: 0.1514653	total: 698ms	remaining: 518ms
287:	learn: 0.1508897	total: 701ms	remaining: 516ms
288:	learn: 0.1502629	total: 703ms	remaining: 513ms
289:	learn: 0.1498131	total: 705ms	remaining: 511ms
290:	learn: 0.1492136	total: 708ms	remaining: 508ms
291:	learn: 0.1485257	total: 710ms	remaining: 506ms
292:	learn: 0.1478877	total: 712ms	remaining: 503ms
293:	learn: 0.1473404	total: 715ms	remaining: 501ms
294:	learn: 0.1467888	total: 717ms	remaining: 498ms
295:	learn: 0.1463668	total: 722ms	remaining: 498ms
296:	learn: 0.1457574	total: 725ms	remaining: 496ms
297:	learn: 0.1453679	total: 728ms	remaining: 493ms
298:	learn: 0.1449307	total: 730ms	remaining: 491ms
299:	learn: 0.1444975	total: 733ms	remaining: 488ms
300:	learn: 0.1438173	total: 735ms	remaining: 486ms
301:	learn: 0.1433622	total: 738ms	remaining: 484ms
302:	learn: 0.1429017	total: 741ms	remaining: 481ms
303:	learn: 0.1422855	total: 743ms	remaining: 479ms
304:	learn: 0.1417801	total: 749ms	remaining: 479ms
305:	learn: 0.1411382	total: 751ms	remaining: 476ms
306:	learn: 0.1408096	total: 754ms	remaining: 474ms
307:	learn: 0.1403508	total: 756ms	remaining: 472ms
308:	learn: 0.1398463	total: 759ms	remaining: 469ms
309:	learn: 0.1392089	total: 762ms	remaining: 467ms
310:	learn: 0.1384594	total: 764ms	remaining: 464ms
311:	learn: 0.1379191	total: 767ms	remaining: 462ms
312:	learn: 0.1373726	total: 769ms	remaining: 460ms
313:	learn: 0.1367998	total: 772ms	remaining: 458ms
314:	learn: 0.1361469	total: 775ms	remaining: 455ms
315:	learn: 0.1354537	total: 778ms	remaining: 453ms
316:	learn: 0.1349819	total: 781ms	remaining: 451ms
317:	learn: 0.1346832	total: 783ms	remaining: 448ms
318:	learn: 0.1342786	total: 786ms	remaining: 446ms
319:	learn: 0.1339238	total: 788ms	remaining: 444ms
320:	learn: 0.1331839	total: 791ms	remaining: 441ms
321:	learn: 0.1326959	total: 794ms	remaining: 439ms
322:	learn: 0.1321077	total: 796ms	remaining: 436ms
323:	learn: 0.1316792	total: 799ms	remaining: 434ms
324:	learn: 0.1312136	total: 801ms	remaining: 432ms
325:	learn: 0.1306990	total: 804ms	remaining: 429ms
326:	learn: 0.1302977	total: 807ms	remaining: 427ms
327:	learn: 0.1298731	total: 809ms	remaining: 424ms
328:	learn: 0.1294692	total: 811ms	remaining: 422ms
329:	learn: 0.1290809	total: 814ms	remaining: 419ms
330:	learn: 0.1286881	total: 818ms	remaining: 417ms
331:	learn: 0.1282603	total: 820ms	remaining: 415ms
332:	learn: 0.1277991	total: 822ms	remaining: 412ms
333:	learn: 0.1273139	total: 825ms	remaining: 410ms
334:	learn: 0.1269262	total: 827ms	remaining: 407ms
335:	learn: 0.1264105	total: 830ms	remaining: 405ms
336:	learn: 0.1259814	total: 832ms	remaining: 402ms
337:	learn: 0.1256140	total: 834ms	remaining: 400ms
338:	learn: 0.1252122	total: 837ms	remaining: 398ms
339:	learn: 0.1247088	total: 841ms	remaining: 396ms
340:	learn: 0.1243904	total: 843ms	remaining: 393ms
341:	learn: 0.1239304	total: 845ms	remaining: 391ms
342:	learn: 0.1234999	total: 848ms	remaining: 388ms
343:	learn: 0.1232434	total: 850ms	remaining: 386ms
344:	learn: 0.1228527	total: 853ms	remaining: 383ms
345:	learn: 0.1224362	total: 855ms	remaining: 381ms
346:	learn: 0.1219923	total: 857ms	remaining: 378ms
347:	learn: 0.1214916	total: 860ms	remaining: 376ms
348:	learn: 0.1210533	total: 862ms	remaining: 373ms
349:	learn: 0.1204470	total: 865ms	remaining: 371ms
350:	learn: 0.1201737	total: 867ms	remaining: 368ms
351:	learn: 0.1197516	total: 869ms	remaining: 366ms
352:	learn: 0.1194263	total: 872ms	remaining: 363ms
353:	learn: 0.1190205	total: 874ms	remaining: 361ms
354:	learn: 0.1185249	total: 876ms	remaining: 358ms
355:	learn: 0.1181984	total: 879ms	remaining: 355ms
356:	learn: 0.1178059	total: 881ms	remaining: 353ms
357:	learn: 0.1174206	total: 884ms	remaining: 350ms
358:	learn: 0.1170685	total: 886ms	remaining: 348ms
359:	learn: 0.1166374	total: 888ms	remaining: 345ms
360:	learn: 0.1161908	total: 891ms	remaining: 343ms
361:	learn: 0.1158647	total: 893ms	remaining: 340ms
362:	learn: 0.1154509	total: 895ms	remaining: 338ms
363:	learn: 0.1150703	total: 898ms	remaining: 335ms
364:	learn: 0.1148172	total: 900ms	remaining: 333ms
365:	learn: 0.1144553	total: 903ms	remaining: 330ms
366:	learn: 0.1140709	total: 905ms	remaining: 328ms
367:	learn: 0.1135735	total: 907ms	remaining: 326ms
368:	learn: 0.1131707	total: 910ms	remaining: 323ms
369:	learn: 0.1127213	total: 912ms	remaining: 321ms
370:	learn: 0.1123463	total: 915ms	remaining: 318ms
371:	learn: 0.1120302	total: 917ms	remaining: 316ms
372:	learn: 0.1114798	total: 920ms	remaining: 313ms
373:	learn: 0.1110113	total: 922ms	remaining: 311ms
374:	learn: 0.1105255	total: 924ms	remaining: 308ms
375:	learn: 0.1101389	total: 927ms	remaining: 306ms
376:	learn: 0.1097696	total: 929ms	remaining: 303ms
377:	learn: 0.1093993	total: 932ms	remaining: 301ms
378:	learn: 0.1091285	total: 934ms	remaining: 298ms
379:	learn: 0.1087640	total: 937ms	remaining: 296ms
380:	learn: 0.1083857	total: 939ms	remaining: 293ms
381:	learn: 0.1080647	total: 941ms	remaining: 291ms
382:	learn: 0.1076851	total: 944ms	remaining: 288ms
383:	learn: 0.1073170	total: 946ms	remaining: 286ms
384:	learn: 0.1069099	total: 949ms	remaining: 283ms
385:	learn: 0.1065470	total: 951ms	remaining: 281ms
386:	learn: 0.1060652	total: 954ms	remaining: 278ms
387:	learn: 0.1057033	total: 956ms	remaining: 276ms
388:	learn: 0.1053999	total: 958ms	remaining: 273ms
389:	learn: 0.1049950	total: 961ms	remaining: 271ms
390:	learn: 0.1045530	total: 963ms	remaining: 268ms
391:	learn: 0.1042686	total: 966ms	remaining: 266ms
392:	learn: 0.1039785	total: 968ms	remaining: 264ms
393:	learn: 0.1036174	total: 971ms	remaining: 261ms
394:	learn: 0.1033338	total: 974ms	remaining: 259ms
395:	learn: 0.1028784	total: 976ms	remaining: 256ms
396:	learn: 0.1023395	total: 979ms	remaining: 254ms
397:	learn: 0.1020510	total: 981ms	remaining: 251ms
398:	learn: 0.1017041	total: 983ms	remaining: 249ms
399:	learn: 0.1014650	total: 986ms	remaining: 246ms
400:	learn: 0.1010774	total: 988ms	remaining: 244ms
401:	learn: 0.1007388	total: 991ms	remaining: 242ms
402:	learn: 0.1002880	total: 994ms	remaining: 239ms
403:	learn: 0.1000308	total: 997ms	remaining: 237ms
404:	learn: 0.0996785	total: 999ms	remaining: 234ms
405:	learn: 0.0993224	total: 1s	remaining: 232ms
406:	learn: 0.0989619	total: 1s	remaining: 229ms
407:	learn: 0.0986197	total: 1.01s	remaining: 227ms
408:	learn: 0.0982579	total: 1.01s	remaining: 225ms
409:	learn: 0.0980286	total: 1.01s	remaining: 222ms
410:	learn: 0.0977019	total: 1.01s	remaining: 220ms
411:	learn: 0.0972947	total: 1.02s	remaining: 217ms
412:	learn: 0.0970134	total: 1.02s	remaining: 215ms
413:	learn: 0.0966933	total: 1.02s	remaining: 212ms
414:	learn: 0.0964287	total: 1.02s	remaining: 210ms
415:	learn: 0.0960277	total: 1.03s	remaining: 207ms
416:	learn: 0.0957220	total: 1.03s	remaining: 205ms
417:	learn: 0.0954410	total: 1.03s	remaining: 203ms
418:	learn: 0.0950936	total: 1.03s	remaining: 200ms
419:	learn: 0.0948343	total: 1.04s	remaining: 198ms
420:	learn: 0.0947129	total: 1.04s	remaining: 195ms
421:	learn: 0.0942402	total: 1.04s	remaining: 193ms
422:	learn: 0.0939689	total: 1.04s	remaining: 190ms
423:	learn: 0.0936835	total: 1.05s	remaining: 188ms
424:	learn: 0.0934437	total: 1.05s	remaining: 185ms
425:	learn: 0.0931401	total: 1.05s	remaining: 183ms
426:	learn: 0.0929102	total: 1.05s	remaining: 180ms
427:	learn: 0.0925280	total: 1.05s	remaining: 178ms
428:	learn: 0.0921188	total: 1.06s	remaining: 175ms
429:	learn: 0.0917053	total: 1.06s	remaining: 173ms
430:	learn: 0.0914572	total: 1.06s	remaining: 170ms
431:	learn: 0.0911954	total: 1.06s	remaining: 168ms
432:	learn: 0.0909965	total: 1.07s	remaining: 165ms
433:	learn: 0.0907931	total: 1.07s	remaining: 163ms
434:	learn: 0.0905364	total: 1.07s	remaining: 160ms
435:	learn: 0.0903101	total: 1.07s	remaining: 158ms
436:	learn: 0.0900253	total: 1.08s	remaining: 155ms
437:	learn: 0.0897750	total: 1.08s	remaining: 153ms
438:	learn: 0.0894928	total: 1.08s	remaining: 150ms
439:	learn: 0.0892117	total: 1.08s	remaining: 148ms
440:	learn: 0.0889646	total: 1.09s	remaining: 145ms
441:	learn: 0.0887429	total: 1.09s	remaining: 143ms
442:	learn: 0.0884198	total: 1.09s	remaining: 140ms
443:	learn: 0.0881309	total: 1.09s	remaining: 138ms
444:	learn: 0.0877848	total: 1.1s	remaining: 135ms
445:	learn: 0.0874884	total: 1.1s	remaining: 133ms
446:	learn: 0.0872598	total: 1.1s	remaining: 131ms
447:	learn: 0.0869812	total: 1.1s	remaining: 128ms
448:	learn: 0.0866572	total: 1.11s	remaining: 126ms
449:	learn: 0.0863391	total: 1.11s	remaining: 123ms
450:	learn: 0.0860751	total: 1.11s	remaining: 121ms
451:	learn: 0.0858080	total: 1.11s	remaining: 118ms
452:	learn: 0.0854266	total: 1.11s	remaining: 116ms
453:	learn: 0.0851223	total: 1.12s	remaining: 113ms
454:	learn: 0.0848741	total: 1.12s	remaining: 111ms
455:	learn: 0.0846077	total: 1.12s	remaining: 108ms
456:	learn: 0.0843928	total: 1.13s	remaining: 106ms
457:	learn: 0.0840349	total: 1.13s	remaining: 103ms
458:	learn: 0.0837811	total: 1.13s	remaining: 101ms
459:	learn: 0.0834910	total: 1.13s	remaining: 98.5ms
460:	learn: 0.0832300	total: 1.13s	remaining: 96ms
461:	learn: 0.0829430	total: 1.14s	remaining: 93.5ms
462:	learn: 0.0827244	total: 1.14s	remaining: 91.1ms
463:	learn: 0.0825013	total: 1.14s	remaining: 88.6ms
464:	learn: 0.0823347	total: 1.14s	remaining: 86.1ms
465:	learn: 0.0821108	total: 1.15s	remaining: 83.7ms
466:	learn: 0.0818449	total: 1.15s	remaining: 81.2ms
467:	learn: 0.0816096	total: 1.15s	remaining: 78.7ms
468:	learn: 0.0813404	total: 1.15s	remaining: 76.2ms
469:	learn: 0.0811016	total: 1.16s	remaining: 73.8ms
470:	learn: 0.0808852	total: 1.16s	remaining: 71.3ms
471:	learn: 0.0805935	total: 1.16s	remaining: 68.9ms
472:	learn: 0.0802630	total: 1.16s	remaining: 66.4ms
473:	learn: 0.0799833	total: 1.17s	remaining: 64ms
474:	learn: 0.0797533	total: 1.17s	remaining: 61.6ms
475:	learn: 0.0795081	total: 1.17s	remaining: 59.1ms
476:	learn: 0.0793212	total: 1.17s	remaining: 56.6ms
477:	learn: 0.0790845	total: 1.18s	remaining: 54.2ms
478:	learn: 0.0787622	total: 1.18s	remaining: 51.7ms
479:	learn: 0.0785081	total: 1.18s	remaining: 49.3ms
480:	learn: 0.0781973	total: 1.18s	remaining: 46.8ms
481:	learn: 0.0778922	total: 1.19s	remaining: 44.3ms
482:	learn: 0.0776508	total: 1.19s	remaining: 41.9ms
483:	learn: 0.0774335	total: 1.19s	remaining: 39.4ms
484:	learn: 0.0772236	total: 1.19s	remaining: 36.9ms
485:	learn: 0.0769365	total: 1.2s	remaining: 34.5ms
486:	learn: 0.0766461	total: 1.2s	remaining: 32ms
487:	learn: 0.0764414	total: 1.2s	remaining: 29.6ms
488:	learn: 0.0761068	total: 1.2s	remaining: 27.1ms
489:	learn: 0.0758575	total: 1.21s	remaining: 24.6ms
490:	learn: 0.0756111	total: 1.21s	remaining: 22.2ms
491:	learn: 0.0754692	total: 1.21s	remaining: 19.7ms
492:	learn: 0.0752304	total: 1.21s	remaining: 17.2ms
493:	learn: 0.0750242	total: 1.22s	remaining: 14.8ms
494:	learn: 0.0748119	total: 1.22s	remaining: 12.3ms
495:	learn: 0.0746124	total: 1.22s	remaining: 9.85ms
496:	learn: 0.0744068	total: 1.22s	remaining: 7.38ms
497:	learn: 0.0741524	total: 1.23s	remaining: 4.92ms
498:	learn: 0.0739137	total: 1.23s	remaining: 2.46ms
499:	learn: 0.0736848	total: 1.23s	remaining: 0us
模型测试集Precision:0.6333333333333333,Recall:0.8636363636363636,F1_score:0.7307692307692307,Accuracy:0.9854469854469855
-------------------测试集混淆举证-------------------
In [56]:
#输出PR曲线和ROC曲线结果
CatBoost_PR.to_excel('Data/PR Curve/ML/CatBoost_PR.xlsx','UTF-8')
CatBoost_ROC.to_excel('Data/ROC Curve/ML/CatBoost_ROC.xlsx','UTF-8')
In [57]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(CatBoost_PR['recall'], CatBoost_PR['precision'],color='red',label='CatBoost=%f'%CatBoost_Accuracy)
PR_ax.plot(DTC_PR['recall'], DTC_PR['precision'],color='green',label='DecisionTree=%f'%DTC_Accuracy)
PR_ax.plot(LightGBM_PR['recall'], LightGBM_PR['precision'],color='blue',label='LightGBM=%f'%LightGBM_Accuracy)
PR_ax.plot(Logistic_PR['recall'], Logistic_PR['precision'],color='yellow',label='Logistic=%f'%Logistic_Accuracy)
PR_ax.plot(RondomForest_PR['recall'], RondomForest_PR['precision'],color='purple',label='RondomForest=%f'%RondomForest_Accuracy)
PR_ax.plot(SVM_PR['recall'], SVM_PR['precision'],color='cyan',label='SVM=%f'%SVM_Accuracy)
PR_ax.plot(XGBoost_PR['recall'], XGBoost_PR['precision'],color='pink',label='XGBoost=%f'%XGBoost_Accuracy)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')  
PR_ax.set_ylabel('Precision')    
plt.legend(loc="best")  
plt.show()
In [58]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(CatBoost_ROC['fpr'], CatBoost_ROC['tpr'],color='red',label='CatBoost=%f'%CatBoost_AUC)
ROC_ax.plot(DTC_ROC['fpr'], DTC_ROC['tpr'],color='green',label='DecisionTree=%f'%DTC_AUC)
ROC_ax.plot(LightGBM_ROC['fpr'], LightGBM_ROC['tpr'],color='blue',label='LightGBM=%f'%LightGBM_AUC)
ROC_ax.plot(Logistic_ROC['fpr'], Logistic_ROC['tpr'],color='yellow',label='Logistic=%f'%Logistic_AUC)
ROC_ax.plot(RondomForest_ROC['fpr'], RondomForest_ROC['tpr'],color='purple',label='RondomForest=%f'%RondomForest_AUC)
ROC_ax.plot(SVM_ROC['fpr'], SVM_ROC['tpr'],color='cyan',label='SVM=%f'%SVM_AUC)
ROC_ax.plot(XGBoost_ROC['fpr'], XGBoost_ROC['tpr'],color='pink',label='XGBoost=%f'%XGBoost_AUC)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])  
ROC_ax.set_ylim([0, 1.05])  
ROC_ax.set_xlabel('FPR')  
ROC_ax.set_ylabel('TPR')    
plt.legend(loc="best")  
plt.show()
In [59]:
#保存模型-输入[batchs,70],输出[batch,1]
MLModel=[Logistic_ML,SVM_ML,DTC_ML,Forest_ML,XGBoost_ML,LightGBM_ML,CatBoost_ML]
MLModelStr=['Logistic.pkl','SVM.pkl','DecisionTree.pkl','Forest.pkl','XGBoost.pkl','LightGBM.pkl','CatBoost.pkl']
try:
    for model,modelstr in zip(MLModel,MLModelStr):
        joblib.dump(model,'ML Models/Trainer/'+modelstr)
    print('模型保存成功!')
except:
    print('模型保存异常!!!')
模型保存成功!

载入SHAP库计算特征重要性

In [126]:
#使用PCA算法计算95%可解释方差比维度数
from sklearn.decomposition import PCA
PCA=PCA(n_components=0.95)
Methylation_99=PCA.fit_transform(MLRunData)    #数据降维
Methylation_99.shape
Out[126]:
(6413, 33)

使用SHAP库计算获得CatBoost模型特征重要性

In [127]:
#复现CatBoost模型训练额测试数据集
CatBoost_train,CatBoost_test,CatBoost_trainlabel,CatBoost_testlabel=train_test_split(MLRunData,ReLabel,stratify=ReLabel,train_size=0.7,random_state=2024)
In [135]:
import shap
CatExplainer=shap.TreeExplainer(CatBoost_ML)
CatBoost_shapvalues=CatExplainer.shap_values(CatBoost_test)
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33)
In [136]:
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33,plot_type='bar')
In [138]:
#绘制heatmap图
SHAP_CatBoost=CatExplainer(CatBoost_test)
plt.figure(dpi=300)
shap.plots.heatmap(SHAP_CatBoost,max_display=34)
plt.show()
In [139]:
#绘制Decision图
expected_value=CatExplainer.expected_value
shap.decision_plot(expected_value,CatBoost_shapvalues,CatBoost_test.columns,feature_display_range=slice(None, -34, -1))
In [144]:
shap.plots.scatter(SHAP_CatBoost[:,'age'])
In [165]:
shap.plots.scatter(SHAP_CatBoost[:,'age'],color=SHAP_CatBoost)
shap.plots.scatter(SHAP_CatBoost[:,'gender_encode'],color=SHAP_CatBoost)
In [143]:
#输出SHAP值
SHAPData=DataFrame(CatBoost_shapvalues)
SHAPData.to_excel('Data/SHAP Values/CatBoost_SHAP_Data.xlsx','UFT-8')
SHAPData.to_csv('Data/SHAP Values/CatBoost_SHAP_Data.csv')

使用LIME(Local Interpretable Model-agnostic Explanations)对SVM模型进行模型解释性。

In [145]:
#转化为Numpy数组
import numpy as np
TestDataLIME=np.array(CatBoost_test)
TestDataLIME
Out[145]:
array([[0.46083596, 0.72218471, 0.67574463, ..., 0.36230225, 0.66666667,
        0.        ],
       [0.38295897, 0.50947919, 0.61430678, ..., 0.46848894, 0.27192982,
        1.        ],
       [0.33958682, 0.69191957, 0.65616158, ..., 0.40703271, 0.72807018,
        1.        ],
       ...,
       [0.33958682, 0.59925265, 0.61928701, ..., 0.34645684, 0.47368421,
        1.        ],
       [0.42417219, 0.72249519, 0.67450701, ..., 0.44286982, 0.18421053,
        0.        ],
       [0.39630923, 0.69161445, 0.66946837, ..., 0.41543005, 0.27116053,
        0.        ]])
In [148]:
#构建LIME解释器
from lime.lime_tabular import LimeTabularExplainer
CatBoost_LIMEExplainer=LimeTabularExplainer(training_data=TestDataLIME,mode='classification',feature_names=CatBoost_test.columns,class_names=[0,1], discretize_continuous=True)
In [149]:
CatBoost_test.shape
Out[149]:
(1924, 72)
In [150]:
#提取卒中患者和健康人数据——为数据生成解释
LIMEData=CatBoost_test
LIMEData['disease_encode']=np.array(CatBoost_testlabel)
#LIMEData=pd.concat([CatBoost_test,CatBoost_testlabel],axis=1,ignore_index=False)    #合并数据
Contral_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==0,:]    #正常组数据
Contral_DataSample=Contral_Data.iloc[0,:-1]
Stroke_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==1,:]    #正常组数据
Stroke_DataSample=Stroke_Data.iloc[1,:-1]
In [163]:
#为健康人生成可解释性
Contral_Explain=CatBoost_LIMEExplainer.explain_instance(Contral_DataSample,CatBoost.predict_proba,num_features=33)
#可视化解释结果
Contral_Explain.show_in_notebook(show_table=True, show_all=False)
Contral_Explain.as_pyplot_figure()
Out[163]:
In [160]:
Control_List=Contral_Explain.as_list()
Control_STR=[]
for feature, weight in Control_List:
    print(f"特征名称: {feature}, 权重: {weight}")
    Control_STR.append(feature)
特征名称: age > 0.63, 权重: 0.14865150574161518
特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425
特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313
特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709
特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463
特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577
特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008
特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517
特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457
特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256
特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686
特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877
特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827
特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702
特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703
特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248
特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338
特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897
特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364
特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131
特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636
特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192
特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126
特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949
特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211
特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464
特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171
特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255
特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191
特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926
特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678
特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464
特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
In [161]:
#为卒中患者生成可解释性
Stroke_Explain=CatBoost_LIMEExplainer.explain_instance(Stroke_DataSample,CatBoost.predict_proba,num_features=29)
#可视化解释结果
Stroke_Explain.show_in_notebook(show_table=True, show_all=False)
Stroke_Explain.as_pyplot_figure()
Out[161]:
In [162]:
Stroke_List=Stroke_Explain.as_list()
Stroke_STR=[]
for feature, weight in Control_List:
    print(f"特征名称: {feature}, 权重: {weight}")
    Stroke_STR.append(feature)
特征名称: age > 0.63, 权重: 0.14865150574161518
特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425
特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313
特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709
特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463
特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577
特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008
特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517
特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457
特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256
特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686
特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877
特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827
特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702
特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703
特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248
特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338
特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897
特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364
特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131
特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636
特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192
特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126
特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949
特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211
特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464
特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171
特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255
特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191
特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926
特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678
特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464
特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
In [164]:
Stroke_STR
Out[164]:
['age > 0.63',
 '0.50 < cg05876899 <= 0.52',
 'cg21459545 > 0.75',
 'cg08291693 <= 0.49',
 '0.79 < cg22328457 <= 0.94',
 'cg01446576 <= 0.53',
 '0.34 < cg00328972 <= 0.36',
 'cg01717973 <= 0.41',
 '0.70 < cg00958409 <= 0.73',
 '0.55 < cg01550055 <= 0.60',
 'cg09134165 > 0.62',
 'cg19214916 > 0.91',
 '0.53 < cg20493887 <= 0.57',
 '0.65 < cg21939789 <= 0.71',
 '0.44 < cg00716675 <= 0.46',
 'cg01101221 <= 0.59',
 '0.62 < cg03977822 <= 0.66',
 'cg12914043 > 0.56',
 'gender_encode <= 0.00',
 'cg09834142 > 0.67',
 '0.85 < cg09229960 <= 0.89',
 'cg15534364 > 0.42',
 '0.66 < cg00817100 <= 0.68',
 '0.55 < cg17488844 <= 0.64',
 '0.31 < cg01522249 <= 0.39',
 'cg27260927 > 0.72',
 '0.55 < cg17917970 <= 0.71',
 'cg27353825 > 0.44',
 'cg19267533 > 0.59',
 'cg00962755 > 0.44',
 'cg25967516 > 0.56',
 '0.61 < cg09060772 <= 0.64',
 '0.41 < cg27636290 <= 0.45']

获取LIME核SHAP数据交集

In [9]:
SHAP_CG=["cg05876899","cg00958409","cg08882363","cg21459545","cg00328972","cg01550055","cg21939789","cg01446576","cg17917970","cg20493887",
         "cg22328457","cg12914043","cg08291693","cg09834142","cg00941229","cg01717973","cg06834235","cg09134165","cg00817100","cg00716675",
         "cg17488844","cg07810091","cg12377701","cg23429746","cg32524254","cg01370179","cg07086565","cg13192819","cg19214916","cg24606370",
         "cg25967516","cg09307104","cg05876899"]
LIME_CG=['cg05876899', 'cg21459545', 'cg08291693', 'cg22328457', 'cg01446576','cg00328972', 'cg01717973', 'cg00958409', 'cg01550055', 'cg09134165',
         'cg19214916', 'cg20493887', 'cg21939789', 'cg00716675', 'cg01101221','cg03977822', 'cg12914043', 'cg09834142', 'cg09229960', 'cg15534364',
         'cg00817100', 'cg17488844', 'cg01522249', 'cg27260927', 'cg17917970','cg27353825', 'cg19267533', 'cg00962755', 'cg25967516', 'cg09060772',
         'cg27636290','cg23429746','cg05876899']
Feature_CG=set(SHAP_CG)&set(LIME_CG)    #计算交集
Feature_CG
Out[9]:
{'cg00328972',
 'cg00716675',
 'cg00817100',
 'cg00958409',
 'cg01446576',
 'cg01550055',
 'cg01717973',
 'cg05876899',
 'cg08291693',
 'cg09134165',
 'cg09834142',
 'cg12914043',
 'cg17488844',
 'cg17917970',
 'cg19214916',
 'cg20493887',
 'cg21459545',
 'cg21939789',
 'cg22328457',
 'cg23429746',
 'cg25967516'}
In [173]:
from matplotlib import pyplot as plt
from matplotlib_venn import venn2
plt.figure(figsize=(8, 8))
venn2([set(SHAP_CG),set(LIME_CG)], set_labels=('Features in SHAP', 'Features in LIME'))
plt.show()

对数据进行统计学分析

In [10]:
StatData=MethylationData.loc[:,list(Feature_CG)+['gender','age','disease']]
StatData.head(10)
Out[10]:
cg00716675 cg00958409 cg01550055 cg17488844 cg08291693 cg25967516 cg21459545 cg09834142 cg19214916 cg05876899 ... cg00817100 cg01717973 cg21939789 cg12914043 cg09134165 cg22328457 cg01446576 gender age disease
0 -3.837361 -2.616463 -1.585160 -1.909169 2.324893 -0.052001 0.384596 -0.601375 0.200630 2.921730 ... -2.010774 -3.052934 -0.164336 -0.866223 -1.288795 -1.564056 -2.570129 F 78.0 stroke
1 -4.247583 -2.767818 -1.247949 -3.938986 3.100385 -1.657722 -0.645381 -3.938986 -1.557086 3.228904 ... -1.543240 -3.886935 -3.314031 -3.790034 -3.256540 0.000000 -3.744756 M 52.0 stroke
2 -3.993781 -2.375206 -1.536363 -4.489850 2.749976 -1.734052 -0.519260 -4.178048 -1.219330 3.076403 ... -1.710727 -5.273603 -2.698069 -3.201956 -3.744756 -3.580953 -4.051632 M 92.0 stroke
3 -3.790034 -2.555085 -1.757764 -1.695385 3.076403 -1.125184 0.281794 -0.795272 -0.376304 3.374769 ... -2.288480 -4.178048 -0.540693 -2.120936 0.048000 -2.441207 -4.585271 F 87.0 stroke
4 -4.402578 -2.388119 -1.726235 -3.580953 3.007447 -2.079779 -0.397057 -3.580953 -1.848299 3.228904 ... -2.337277 -3.993781 -2.785946 -3.256540 -3.149987 -3.744756 -4.247583 M 87.0 stroke
5 -3.790034 -2.230092 -1.253727 -4.322159 2.881228 -1.856830 0.003999 -2.985388 -1.185564 3.314031 ... -2.020410 -4.112908 -2.648415 -2.804368 -3.100385 -3.701353 -4.112908 M 41.0 stroke
6 -3.790034 -2.715111 -1.271175 -0.772045 2.767818 -0.954183 0.519260 0.019997 -0.924494 3.124904 ... -1.900305 -3.837361 -0.401218 -1.093021 0.156286 -1.114405 -4.585271 F 78.0 stroke
7 -2.749976 -2.749976 -1.324576 -3.790034 3.701353 -2.441207 -0.654254 -3.580953 -2.496772 3.284902 ... -1.330612 -4.051632 -2.482634 -3.837361 -3.076403 -3.314031 -4.322159 M 80.0 stroke
8 -2.427712 -2.648415 -1.157883 -4.690541 3.314031 -2.804368 -0.426255 -4.247583 -1.613763 3.201956 ... -1.146922 -4.322159 -2.401172 -3.406479 -3.406479 0.000000 -4.247583 M 69.0 stroke
9 -2.842141 -2.540233 -0.974195 -5.773449 3.256540 -2.511081 0.084033 -4.112908 -1.373471 3.543689 ... -1.348848 -3.886935 -2.570129 -3.100385 0.000000 0.000000 -3.659672 M 75.0 stroke

10 rows × 24 columns

In [180]:
#进行正态性检验-基于D’Agostino and Pearson’s test 
from scipy.stats import normaltest
Normal_Name,Normal_stat,Normal_Pvalue=[],[],[]
for normal_name in list(Feature_CG)+['age']:
    stats,p_value=normaltest(StatData.loc[:,normal_name])    #正态检验
    Normal_Name.append(normal_name)
    Normal_stat.append(stats)
    Normal_Pvalue.append(p_value)
NormalResut=DataFrame()
NormalResut['Features']=Normal_Name
NormalResut['Stats']=Normal_stat
NormalResut['P value']=Normal_Pvalue
NormalResut
Out[180]:
Features Stats P value
0 cg23429746 128338.014578 0.000000e+00
1 cg19214916 2722.890099 0.000000e+00
2 cg01717973 2679.681287 0.000000e+00
3 cg21459545 3691.925554 0.000000e+00
4 cg20493887 621.263340 1.242739e-135
5 cg01550055 881.708181 3.463389e-192
6 cg01446576 3764.295928 0.000000e+00
7 cg05876899 1797.616451 0.000000e+00
8 cg00817100 5265.657589 0.000000e+00
9 cg09134165 1570.401594 0.000000e+00
10 cg09834142 4664.772845 0.000000e+00
11 cg08291693 3407.019911 0.000000e+00
12 cg17917970 943.887704 1.089906e-205
13 cg25967516 335.598808 1.335503e-73
14 cg22328457 701.152844 5.579490e-153
15 cg00958409 7688.564578 0.000000e+00
16 cg12914043 380.433626 2.454074e-83
17 cg00716675 5795.308748 0.000000e+00
18 cg17488844 865.716145 1.028318e-188
19 cg21939789 46963.872736 0.000000e+00
20 cg00328972 3194.712049 0.000000e+00
21 age 1563.362527 0.000000e+00
In [181]:
NormalResut.to_excel('Data/Analysis Data/NormalTest.xlsx')    #输出正态检验结果
In [185]:
#绘制分布图
import seaborn as sns
for name in list(Feature_CG)+['age']:
    kde=plt.figure()
    sns.distplot(StatData.loc[:,name])
    plt.show()

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

In [189]:
#使用Mann-Whitney U对卒中患者、对照组进行差异分析
from scipy.stats import mannwhitneyu,kruskal
Diff_name,Mann_W_stats,Mann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Control=StatData.loc[StatData.loc[:,'disease']=='control',:]
for name in list(Feature_CG)+['age']:
    mann_stats,mann_pvalue=mannwhitneyu(Stroke.loc[:,name],Control.loc[:,name],alternative='two-sided')
    Diff_name.append(name)
    Mann_W_stats.append(mann_stats)
    Mann_W_pvalue.append(mann_pvalue)
DiffResult=DataFrame()
DiffResult['Features']=Diff_name
DiffResult['Mann-Whitney U Stats']=Mann_W_stats
DiffResult['Mann-Whitney U Pvalue']=Mann_W_pvalue
DiffResult=DiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
DiffResult
Out[189]:
Features Mann-Whitney U Stats Mann-Whitney U Pvalue
7 cg05876899 757768.0 6.339030e-41
21 age 746114.5 6.539961e-38
15 cg00958409 212382.0 4.768737e-29
3 cg21459545 223154.5 1.027115e-26
5 cg01550055 683678.0 8.623696e-24
14 cg22328457 267557.0 4.269751e-19
6 cg01446576 283942.5 1.678612e-15
12 cg17917970 300048.5 2.240491e-13
9 cg09134165 309546.0 4.232856e-12
20 cg00328972 315722.5 6.231854e-11
2 cg01717973 591836.5 3.151485e-09
11 cg08291693 341291.0 7.604157e-08
0 cg23429746 367458.5 2.721422e-05
1 cg19214916 377703.0 1.885910e-04
8 cg00817100 536568.5 6.124156e-04
4 cg20493887 395897.5 3.540958e-03
17 cg00716675 415576.0 4.261281e-02
13 cg25967516 497331.5 9.736992e-02
16 cg12914043 496806.5 1.022572e-01
18 cg17488844 488567.0 2.066917e-01
10 cg09834142 486461.0 2.428824e-01
19 cg21939789 476969.0 4.592971e-01
In [190]:
DiffResult.to_excel('Data/Analysis Data/StrokeVSControl.xlsx')
In [216]:
#数据标准化和缩放
CorrData=StatData.loc[:,['age','cg05876899']]
Corr_name=CorrData.columns
CorrStand=StandardScaler()    #数据标准化
CorrStand=CorrStand.fit_transform(CorrData)
MinMaxCorr=MinMaxScaler(feature_range=(0,1))    #数据缩放
MinMax=MinMaxCorr.fit_transform(CorrStand)
CorrDataST=DataFrame(MinMax)
CorrDataST.columns=Corr_name
CorrDataST
Out[216]:
age cg05876899
0 0.684211 0.495571
1 0.456140 0.520210
2 0.807018 0.507977
3 0.763158 0.531910
4 0.763158 0.520210
... ... ...
6408 0.000000 0.430502
6409 0.652632 0.511868
6410 0.605263 0.502446
6411 0.298246 0.534453
6412 0.131579 0.515939

6413 rows × 2 columns

In [218]:
#绘制age与cg05876899散点图
from scipy.stats import spearmanr
corr,p_value=spearmanr(CorrDataST.loc[:,'age'],CorrDataST.loc[:,'cg05876899'])
print('Spearman相关系数为:{0},P值为:{1}'.format(corr,p_value))
plt.figure(dpi=300)
sns.jointplot(data=CorrDataST,x='age',y='cg05876899')
plt.show()
Spearman相关系数为:-0.09646944046883249,P值为:9.790722731741588e-15
<Figure size 1800x1200 with 0 Axes>
In [209]:
plt.figure(dpi=300)
sns.barplot(StatData,x='disease',y='cg05876899',errorbar="ci")
plt.show()
In [191]:
#绘制箱型图
for name in list(DiffResult['Features']):
    Box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=name,showmeans=True)
    plt.show()
In [195]:
#使用Mann-Whitney U对男性患者和女性患者进行差异分析
from scipy.stats import mannwhitneyu,kruskal
SexDiff_name,SexMann_W_stats,SexMann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Man=Stroke.loc[Stroke.loc[:,'gender']=='M',:]
Feman=Stroke.loc[Stroke.loc[:,'gender']=='F',:]
for name in list(Feature_CG)+['age']:
    mann_stats,mann_pvalue=mannwhitneyu(Man.loc[:,name],Feman.loc[:,name],alternative='two-sided')
    SexDiff_name.append(name)
    SexMann_W_stats.append(mann_stats)
    SexMann_W_pvalue.append(mann_pvalue)
SexDiffResult=DataFrame()
SexDiffResult['Features']=SexDiff_name
SexDiffResult['Mann-Whitney U Stats']=SexMann_W_stats
SexDiffResult['Mann-Whitney U Pvalue']=SexMann_W_pvalue
SexDiffResult=SexDiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
SexDiffResult
Out[195]:
Features Mann-Whitney U Stats Mann-Whitney U Pvalue
0 cg23429746 55.0 1.184262e-24
10 cg09834142 57.0 1.280125e-24
18 cg17488844 57.5 1.295483e-24
19 cg21939789 71.0 2.245348e-24
3 cg21459545 72.5 2.394466e-24
16 cg12914043 91.0 4.948103e-24
13 cg25967516 129.5 2.259475e-23
1 cg19214916 156.5 6.443147e-23
9 cg09134165 202.0 3.474110e-22
12 cg17917970 310.0 1.973555e-20
14 cg22328457 1397.5 4.413451e-07
21 age 1713.5 1.300201e-04
11 cg08291693 3530.5 1.314165e-03
7 cg05876899 3309.5 1.841224e-02
4 cg20493887 2157.5 3.532568e-02
15 cg00958409 3086.5 1.356811e-01
2 cg01717973 2411.5 2.623936e-01
20 cg00328972 2863.0 5.301055e-01
5 cg01550055 2559.5 5.848453e-01
8 cg00817100 2790.0 7.316775e-01
17 cg00716675 2656.0 8.630851e-01
6 cg01446576 2742.5 8.735049e-01
In [196]:
SexDiffResult.to_excel('Data/Analysis Data/SexDiffResult.xlsx')
In [210]:
plt.figure(dpi=300)
sns.barplot(Stroke,x='gender',y='cg23429746',errorbar="ci")
plt.show()
In [197]:
#绘制箱型图
for name in SexDiffResult['Features']:
    Box=plt.figure(dpi=300)
    sns.boxplot(Stroke,x='gender',y=name,showmeans=True)
    plt.show()
In [12]:
import matplotlib.pyplot as plt
import seaborn as sns
In [14]:
for cg_name in list(Feature_CG)+['age']:
    box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
    plt.show()
In [16]:
#提取卒中患者VS对照组与男性患者与女性患者甲基化数据取交集_[Top 10]
CG_StrokeVSControl=['cg05876899', 'cg00958409', 'cg21459545', 'cg01550055', 'cg22328457', 'cg01446576', 'cg17917970', 
                    'cg09134165', 'cg00328972','cg01717973']
CG_StrokeManVSFeman=['cg23429746', 'cg09834142', 'cg17488844', 'cg21939789', 'cg21459545', 'cg12914043', 'cg25967516', 
                     'cg19214916', 'cg09134165', 'cg17917970']
Get_DiffCG=set(CG_StrokeVSControl)&set(CG_StrokeManVSFeman)
print(Get_DiffCG)
{'cg09134165', 'cg21459545', 'cg17917970'}
In [199]:
#绘制韦恩图
plt.figure(figsize=(6,8))
venn2([set(CG_StrokeVSControl),set(CG_StrokeManVSFeman)],set_labels=('Stroke group vs control group','Male stroke group vs female stroke group'))
plt.show()
In [17]:
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
    box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
    plt.show()
In [207]:
#绘制条形图
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
    box=plt.figure(dpi=300)
    sns.barplot(StatData,x='disease',y=cg_name,hue='gender',errorbar="ci") #95%CI
    plt.show()
In [ ]: